class SAC(OffPolicyRLModel):
"""
Soft Actor-Critic (SAC)
Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,
This implementation borrows code from original implementation (https://github.com/haarnoja/sac)
from OpenAI Spinning Up (https://github.com/openai/spinningup) and from the Softlearning repo
(https://github.com/rail-berkeley/softlearning/)
Paper: https://arxiv.org/abs/1801.01290
Introduction to SAC: https://spinningup.openai.com/en/latest/algorithms/sac.html
:param policy: (SACPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, LnMlpPolicy, ...)
:param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
:param gamma: (float) the discount factor
:param learning_rate: (float or callable) learning rate for adam optimizer,
the same learning rate will be used for all networks (Q-Values, Actor and Value function)
it can be a function of the current progress (from 1 to 0)
:param buffer_size: (int) size of the replay buffer
:param batch_size: (int) Minibatch size for each gradient update
:param tau: (float) the soft update coefficient ("polyak update", between 0 and 1)
:param ent_coef: (str or float) Entropy regularization coefficient. (Equivalent to
inverse of reward scale in the original SAC paper.) Controlling exploration/exploitation trade-off.
Set it to 'auto' to learn it automatically (and 'auto_0.1' for using 0.1 as initial value)
:param train_freq: (int) Update the model every `train_freq` steps.
:param learning_starts: (int) how many steps of the model to collect transitions for before learning starts
:param target_update_interval: (int) update the target network every `target_network_update_freq` steps.
:param gradient_steps: (int) How many gradient update after each step
:param target_entropy: (str or float) target entropy when learning ent_coef (ent_coef = 'auto')
:param action_noise: (ActionNoise) the action noise type (None by default), this can help
for hard exploration problem. Cf DDPG for the different action noise type.
:param random_exploration: (float) Probability of taking a random action (as in an epsilon-greedy strategy)
This is not needed for SAC normally but can help exploring when using HER + SAC.
This hack was present in the original OpenAI Baselines repo (DDPG + HER)
:param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
:param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
:param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
:param policy_kwargs: (dict) additional arguments to be passed to the policy on creation
:param full_tensorboard_log: (bool) enable additional logging when using tensorboard
Note: this has no effect on SAC logging for now
:param seed: (int) Seed for the pseudo-random generators (python, numpy, tensorflow).
If None (default), use random seed. Note that if you want completely deterministic
results, you must set `n_cpu_tf_sess` to 1.
:param n_cpu_tf_sess: (int) The number of threads for TensorFlow operations
If None, the number of cpu of the current machine will be used.
"""
def __init__(self,
policy,
env,
gamma=0.99,
learning_rate=3e-4,
buffer_size=50000,
learning_starts=100,
train_freq=1,
batch_size=64,
tau=0.005,
ent_coef='auto',
target_update_interval=1,
gradient_steps=1,
target_entropy='auto',
action_noise=None,
random_exploration=0.0,
verbose=0,
tensorboard_log=None,
_init_setup_model=True,
policy_kwargs=None,
full_tensorboard_log=False,
seed=None,
n_cpu_tf_sess=None):
super(SAC, self).__init__(policy=policy,
env=env,
replay_buffer=None,
verbose=verbose,
policy_base=SACPolicy,
requires_vec_env=False,
policy_kwargs=policy_kwargs,
seed=seed,
n_cpu_tf_sess=n_cpu_tf_sess)
self.buffer_size = buffer_size
self.learning_rate = learning_rate
self.learning_starts = learning_starts
self.train_freq = train_freq
self.batch_size = batch_size
self.tau = tau
# In the original paper, same learning rate is used for all networks
# self.policy_lr = learning_rate
# self.qf_lr = learning_rate
# self.vf_lr = learning_rate
# Entropy coefficient / Entropy temperature
# Inverse of the reward scale
self.ent_coef = ent_coef
self.target_update_interval = target_update_interval
self.gradient_steps = gradient_steps
self.gamma = gamma
self.action_noise = action_noise
self.random_exploration = random_exploration
self.value_fn = None
self.graph = None
self.replay_buffer = None
self.episode_reward = None
self.sess = None
self.tensorboard_log = tensorboard_log
self.verbose = verbose
self.params = None
self.summary = None
self.policy_tf = None
self.target_entropy = target_entropy
self.full_tensorboard_log = full_tensorboard_log
self.obs_target = None
self.target_policy = None
self.actions_ph = None
self.rewards_ph = None
self.terminals_ph = None
self.observations_ph = None
self.action_target = None
self.next_observations_ph = None
self.value_target = None
self.step_ops = None
self.target_update_op = None
self.infos_names = None
self.entropy = None
self.target_params = None
self.learning_rate_ph = None
self.processed_obs_ph = None
self.processed_next_obs_ph = None
self.log_ent_coef = None
if _init_setup_model:
self.setup_model()
def _get_pretrain_placeholders(self):
policy = self.policy_tf
# Rescale
deterministic_action = unscale_action(self.action_space,
self.deterministic_action)
return policy.obs_ph, self.actions_ph, deterministic_action
def setup_model(self):
with SetVerbosity(self.verbose):
self.graph = tf.Graph()
with self.graph.as_default():
self.set_random_seed(self.seed)
self.sess = tf_util.make_session(num_cpu=self.n_cpu_tf_sess,
graph=self.graph)
self.replay_buffer = ReplayBuffer(self.buffer_size)
with tf.variable_scope("input", reuse=False):
# Create policy and target TF objects
self.policy_tf = self.policy(self.sess,
self.observation_space,
self.action_space,
**self.policy_kwargs)
self.target_policy = self.policy(self.sess,
self.observation_space,
self.action_space,
**self.policy_kwargs)
# Initialize Placeholders
self.observations_ph = self.policy_tf.obs_ph
# Normalized observation for pixels
self.processed_obs_ph = self.policy_tf.processed_obs
self.next_observations_ph = self.target_policy.obs_ph
self.processed_next_obs_ph = self.target_policy.processed_obs
self.action_target = self.target_policy.action_ph
self.terminals_ph = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals')
self.rewards_ph = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.actions_ph = tf.placeholder(tf.float32,
shape=(None, ) +
self.action_space.shape,
name='actions')
self.learning_rate_ph = tf.placeholder(
tf.float32, [], name="learning_rate_ph")
with tf.variable_scope("model", reuse=False):
# Create the policy
# first return value corresponds to deterministic actions
# policy_out corresponds to stochastic actions, used for training
# logp_pi is the log probability of actions taken by the policy
self.deterministic_action, policy_out, logp_pi = self.policy_tf.make_actor(
self.processed_obs_ph)
# Monitor the entropy of the policy,
# this is not used for training
self.entropy = tf.reduce_mean(self.policy_tf.entropy)
# Use two Q-functions to improve performance by reducing overestimation bias.
qf1, qf2, value_fn = self.policy_tf.make_critics(
self.processed_obs_ph,
self.actions_ph,
create_qf=True,
create_vf=True) # Q(s,a)
qf1_pi, qf2_pi, _ = self.policy_tf.make_critics(
self.processed_obs_ph,
policy_out,
create_qf=True,
create_vf=False,
reuse=True) # Q(s, pi(a|s))
# Target entropy is used when learning the entropy coefficient
if self.target_entropy == 'auto':
# automatically set target entropy if needed
self.target_entropy = -np.prod(
self.env.action_space.shape).astype(np.float32)
else:
# Force conversion
# this will also throw an error for unexpected string
self.target_entropy = float(self.target_entropy)
# The entropy coefficient or entropy can be learned automatically
# see Automating Entropy Adjustment for Maximum Entropy RL section
# of https://arxiv.org/abs/1812.05905
if isinstance(self.ent_coef,
str) and self.ent_coef.startswith('auto'):
# Default initial value of ent_coef when learned
init_value = 1.0
if '_' in self.ent_coef:
init_value = float(self.ent_coef.split('_')[1])
assert init_value > 0., "The initial value of ent_coef must be greater than 0"
self.log_ent_coef = tf.get_variable(
'log_ent_coef',
dtype=tf.float32,
initializer=np.log(init_value).astype(np.float32))
self.ent_coef = tf.exp(self.log_ent_coef)
else:
# Force conversion to float
# this will throw an error if a malformed string (different from 'auto')
# is passed
self.ent_coef = float(self.ent_coef)
with tf.variable_scope("target", reuse=False):
# Create the value network
_, _, value_target = self.target_policy.make_critics(
self.processed_next_obs_ph,
create_qf=False,
create_vf=True)
self.value_target = value_target
with tf.variable_scope("loss", reuse=False):
# Take the min of the two Q-Values (Double-Q Learning)
min_qf_pi = tf.minimum(qf1_pi, qf2_pi)
# Target for Q value regression
q_backup = tf.stop_gradient(self.rewards_ph +
(1 - self.terminals_ph) *
self.gamma * self.value_target)
# Compute Q-Function loss
# TODO: test with huber loss (it would avoid too high values)
qf1_loss = 0.5 * tf.reduce_mean((q_backup - qf1)**2)
qf2_loss = 0.5 * tf.reduce_mean((q_backup - qf2)**2)
# Compute the entropy temperature loss
# it is used when the entropy coefficient is learned
ent_coef_loss, entropy_optimizer = None, None
if not isinstance(self.ent_coef, float):
ent_coef_loss = -tf.reduce_mean(
self.log_ent_coef *
tf.stop_gradient(logp_pi + self.target_entropy))
entropy_optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate_ph)
# Compute the policy loss
# Alternative: policy_kl_loss = tf.reduce_mean(logp_pi - min_qf_pi)
policy_kl_loss = tf.reduce_mean(self.ent_coef * logp_pi -
qf1_pi)
# NOTE: in the original implementation, they have an additional
# regularization loss for the Gaussian parameters
# this is not used for now
# policy_loss = (policy_kl_loss + policy_regularization_loss)
policy_loss = policy_kl_loss
# Target for value fn regression
# We update the vf towards the min of two Q-functions in order to
# reduce overestimation bias from function approximation error.
v_backup = tf.stop_gradient(min_qf_pi -
self.ent_coef * logp_pi)
value_loss = 0.5 * tf.reduce_mean((value_fn - v_backup)**2)
values_losses = qf1_loss + qf2_loss + value_loss
# Policy train op
# (has to be separate from value train op, because min_qf_pi appears in policy_loss)
policy_optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate_ph)
policy_train_op = policy_optimizer.minimize(
policy_loss, var_list=get_vars('model/pi'))
# Value train op
value_optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate_ph)
values_params = get_vars('model/values_fn')
source_params = get_vars("model/values_fn/vf")
target_params = get_vars("target/values_fn/vf")
# Polyak averaging for target variables
self.target_update_op = [
tf.assign(target,
(1 - self.tau) * target + self.tau * source)
for target, source in zip(target_params, source_params)
]
# Initializing target to match source variables
target_init_op = [
tf.assign(target, source)
for target, source in zip(target_params, source_params)
]
# Control flow is used because sess.run otherwise evaluates in nondeterministic order
# and we first need to compute the policy action before computing q values losses
with tf.control_dependencies([policy_train_op]):
train_values_op = value_optimizer.minimize(
values_losses, var_list=values_params)
self.infos_names = [
'policy_loss', 'qf1_loss', 'qf2_loss',
'value_loss', 'entropy'
]
# All ops to call during one training step
self.step_ops = [
policy_loss, qf1_loss, qf2_loss, value_loss, qf1,
qf2, value_fn, logp_pi, self.entropy,
policy_train_op, train_values_op
]
# Add entropy coefficient optimization operation if needed
if ent_coef_loss is not None:
with tf.control_dependencies([train_values_op]):
ent_coef_op = entropy_optimizer.minimize(
ent_coef_loss, var_list=self.log_ent_coef)
self.infos_names += [
'ent_coef_loss', 'ent_coef'
]
self.step_ops += [
ent_coef_op, ent_coef_loss, self.ent_coef
]
# Monitor losses and entropy in tensorboard
tf.summary.scalar('policy_loss', policy_loss)
tf.summary.scalar('qf1_loss', qf1_loss)
tf.summary.scalar('qf2_loss', qf2_loss)
tf.summary.scalar('value_loss', value_loss)
tf.summary.scalar('entropy', self.entropy)
if ent_coef_loss is not None:
tf.summary.scalar('ent_coef_loss', ent_coef_loss)
tf.summary.scalar('ent_coef', self.ent_coef)
tf.summary.scalar('learning_rate',
tf.reduce_mean(self.learning_rate_ph))
# Retrieve parameters that must be saved
self.params = get_vars("model")
self.target_params = get_vars("target/values_fn/vf")
# Initialize Variables and target network
with self.sess.as_default():
self.sess.run(tf.global_variables_initializer())
self.sess.run(target_init_op)
self.summary = tf.summary.merge_all()
def _train_step(self, step, writer, learning_rate):
# Sample a batch from the replay buffer
batch = self.replay_buffer.sample(self.batch_size)
batch_obs, batch_actions, batch_rewards, batch_next_obs, batch_dones = batch
feed_dict = {
self.observations_ph: batch_obs,
self.actions_ph: batch_actions,
self.next_observations_ph: batch_next_obs,
self.rewards_ph: batch_rewards.reshape(self.batch_size, -1),
self.terminals_ph: batch_dones.reshape(self.batch_size, -1),
self.learning_rate_ph: learning_rate
}
# out = [policy_loss, qf1_loss, qf2_loss,
# value_loss, qf1, qf2, value_fn, logp_pi,
# self.entropy, policy_train_op, train_values_op]
# Do one gradient step
# and optionally compute log for tensorboard
if writer is not None:
out = self.sess.run([self.summary] + self.step_ops, feed_dict)
summary = out.pop(0)
writer.add_summary(summary, step)
else:
out = self.sess.run(self.step_ops, feed_dict)
# Unpack to monitor losses and entropy
policy_loss, qf1_loss, qf2_loss, value_loss, *values = out
# qf1, qf2, value_fn, logp_pi, entropy, *_ = values
entropy = values[4]
if self.log_ent_coef is not None:
ent_coef_loss, ent_coef = values[-2:]
return policy_loss, qf1_loss, qf2_loss, value_loss, entropy, ent_coef_loss, ent_coef
return policy_loss, qf1_loss, qf2_loss, value_loss, entropy
def learn(self,
total_timesteps,
callback=None,
log_interval=4,
tb_log_name="SAC",
reset_num_timesteps=True,
replay_wrapper=None):
new_tb_log = self._init_num_timesteps(reset_num_timesteps)
if replay_wrapper is not None:
self.replay_buffer = replay_wrapper(self.replay_buffer)
with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \
as writer:
self._setup_learn()
# Transform to callable if needed
self.learning_rate = get_schedule_fn(self.learning_rate)
# Initial learning rate
current_lr = self.learning_rate(1)
start_time = time.time()
episode_rewards = [0.0]
episode_successes = []
if self.action_noise is not None:
self.action_noise.reset()
obs = self.env.reset()
self.episode_reward = np.zeros((1, ))
ep_info_buf = deque(maxlen=100)
n_updates = 0
infos_values = []
for step in range(total_timesteps):
if callback is not None:
# Only stop training if return value is False, not when it is None. This is for backwards
# compatibility with callbacks that have no return statement.
if callback(locals(), globals()) is False:
break
# Before training starts, randomly sample actions
# from a uniform distribution for better exploration.
# Afterwards, use the learned policy
# if random_exploration is set to 0 (normal setting)
if self.num_timesteps < self.learning_starts or np.random.rand(
) < self.random_exploration:
# actions sampled from action space are from range specific to the environment
# but algorithm operates on tanh-squashed actions therefore simple scaling is used
unscaled_action = self.env.action_space.sample()
action = scale_action(self.action_space, unscaled_action)
else:
action = self.policy_tf.step(
obs[None], deterministic=False).flatten()
# Add noise to the action (improve exploration,
# not needed in general)
if self.action_noise is not None:
action = np.clip(action + self.action_noise(), -1, 1)
# inferred actions need to be transformed to environment action_space before stepping
unscaled_action = unscale_action(self.action_space, action)
assert action.shape == self.env.action_space.shape
new_obs, reward, done, info = self.env.step(unscaled_action)
# Store transition in the replay buffer.
self.replay_buffer.add(obs, action, reward, new_obs,
float(done))
obs = new_obs
# Retrieve reward and episode length if using Monitor wrapper
maybe_ep_info = info.get('episode')
if maybe_ep_info is not None:
ep_info_buf.extend([maybe_ep_info])
if writer is not None:
# Write reward per episode to tensorboard
ep_reward = np.array([reward]).reshape((1, -1))
ep_done = np.array([done]).reshape((1, -1))
self.episode_reward = total_episode_reward_logger(
self.episode_reward, ep_reward, ep_done, writer,
self.num_timesteps)
if step % self.train_freq == 0:
mb_infos_vals = []
# Update policy, critics and target networks
for grad_step in range(self.gradient_steps):
# Break if the warmup phase is not over
# or if there are not enough samples in the replay buffer
if not self.replay_buffer.can_sample(self.batch_size) \
or self.num_timesteps < self.learning_starts:
break
n_updates += 1
# Compute current learning_rate
frac = 1.0 - step / total_timesteps
current_lr = self.learning_rate(frac)
# Update policy and critics (q functions)
mb_infos_vals.append(
self._train_step(step, writer, current_lr))
# Update target network
if (step +
grad_step) % self.target_update_interval == 0:
# Update target network
self.sess.run(self.target_update_op)
# Log losses and entropy, useful for monitor training
if len(mb_infos_vals) > 0:
infos_values = np.mean(mb_infos_vals, axis=0)
episode_rewards[-1] += reward
if done:
if self.action_noise is not None:
self.action_noise.reset()
if not isinstance(self.env, VecEnv):
obs = self.env.reset()
episode_rewards.append(0.0)
maybe_is_success = info.get('is_success')
if maybe_is_success is not None:
episode_successes.append(float(maybe_is_success))
if len(episode_rewards[-101:-1]) == 0:
mean_reward = -np.inf
else:
mean_reward = round(
float(np.mean(episode_rewards[-101:-1])), 1)
num_episodes = len(episode_rewards)
self.num_timesteps += 1
# Display training infos
if self.verbose >= 1 and done and log_interval is not None and len(
episode_rewards) % log_interval == 0:
fps = int(step / (time.time() - start_time))
logger.logkv("episodes", num_episodes)
logger.logkv("mean 100 episode reward", mean_reward)
logger.logkv("episode reward", episode_rewards[-2])
if len(ep_info_buf) > 0 and len(ep_info_buf[0]) > 0:
logger.logkv(
'ep_rewmean',
safe_mean(
[ep_info['r'] for ep_info in ep_info_buf]))
logger.logkv(
'eplenmean',
safe_mean(
[ep_info['l'] for ep_info in ep_info_buf]))
logger.logkv("n_updates", n_updates)
logger.logkv("current_lr", current_lr)
logger.logkv("fps", fps)
logger.logkv('time_elapsed', int(time.time() - start_time))
if len(episode_successes) > 0:
logger.logkv("success rate",
np.mean(episode_successes[-100:]))
if len(infos_values) > 0:
for (name, val) in zip(self.infos_names, infos_values):
logger.logkv(name, val)
logger.logkv("total timesteps", self.num_timesteps)
logger.dumpkvs()
# Reset infos:
infos_values = []
return self
def action_probability(self,
observation,
state=None,
mask=None,
actions=None,
logp=False):
if actions is not None:
raise ValueError("Error: SAC does not have action probabilities.")
warnings.warn(
"Even though SAC has a Gaussian policy, it cannot return a distribution as it "
"is squashed by a tanh before being scaled and outputed.")
return None
def predict(self, observation, state=None, mask=None, deterministic=True):
observation = np.array(observation)
vectorized_env = self._is_vectorized_observation(
observation, self.observation_space)
observation = observation.reshape((-1, ) +
self.observation_space.shape)
actions = self.policy_tf.step(observation, deterministic=deterministic)
actions = actions.reshape(
(-1, ) +
self.action_space.shape) # reshape to the correct action shape
actions = unscale_action(
self.action_space, actions) # scale the output for the prediction
if not vectorized_env:
actions = actions[0]
return actions, None
def get_parameter_list(self):
return (self.params + self.target_params)
def save(self, save_path, cloudpickle=False):
data = {
"learning_rate": self.learning_rate,
"buffer_size": self.buffer_size,
"learning_starts": self.learning_starts,
"train_freq": self.train_freq,
"batch_size": self.batch_size,
"tau": self.tau,
"ent_coef":
self.ent_coef if isinstance(self.ent_coef, float) else 'auto',
"target_entropy": self.target_entropy,
# Should we also store the replay buffer?
# this may lead to high memory usage
# with all transition inside
# "replay_buffer": self.replay_buffer
"gamma": self.gamma,
"verbose": self.verbose,
"observation_space": self.observation_space,
"action_space": self.action_space,
"policy": self.policy,
"n_envs": self.n_envs,
"n_cpu_tf_sess": self.n_cpu_tf_sess,
"seed": self.seed,
"action_noise": self.action_noise,
"random_exploration": self.random_exploration,
"_vectorize_action": self._vectorize_action,
"policy_kwargs": self.policy_kwargs
}
params_to_save = self.get_parameters()
self._save_to_file(save_path,
data=data,
params=params_to_save,
cloudpickle=cloudpickle)
class DQNUniqueActions(DQN):
"""Subclass with a modified "learn" method which only allows actions
to be taken once per episode.
"""
def learn(self,
total_timesteps,
callback=None,
log_interval=100,
tb_log_name="DQN",
reset_num_timesteps=True,
replay_wrapper=None):
new_tb_log = self._init_num_timesteps(reset_num_timesteps)
with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \
as writer:
self._setup_learn()
# Create the replay buffer
if self.prioritized_replay:
self.replay_buffer = PrioritizedReplayBuffer(
self.buffer_size, alpha=self.prioritized_replay_alpha)
if self.prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
else:
prioritized_replay_beta_iters = self.prioritized_replay_beta_iters
self.beta_schedule = LinearSchedule(
prioritized_replay_beta_iters,
initial_p=self.prioritized_replay_beta0,
final_p=1.0)
else:
self.replay_buffer = ReplayBuffer(self.buffer_size)
self.beta_schedule = None
if replay_wrapper is not None:
assert not self.prioritized_replay, "Prioritized replay buffer is not supported by HER"
self.replay_buffer = replay_wrapper(self.replay_buffer)
# Create the schedule for exploration starting from 1.
self.exploration = LinearSchedule(
schedule_timesteps=int(self.exploration_fraction *
total_timesteps),
initial_p=self.exploration_initial_eps,
final_p=self.exploration_final_eps)
episode_rewards = [0.0]
episode_successes = []
obs = self.env.reset()
reset = True
############################################################
# MODIFICATION:
# Track list of actions taken each episode. This is
# intentionally not a set so that we can use np.isin.
action_list = list()
############################################################
for _ in range(total_timesteps):
if callback is not None:
# Only stop training if return value is False, not when it is None. This is for backwards
# compatibility with callbacks that have no return statement.
if callback(locals(), globals()) is False:
break
# Take action and update exploration to the newest value
kwargs = {}
if not self.param_noise:
update_eps = self.exploration.value(self.num_timesteps)
update_param_noise_threshold = 0.
else:
update_eps = 0.
# Compute the threshold such that the KL divergence between perturbed and non-perturbed
# policy is comparable to eps-greedy exploration with eps = exploration.value(t).
# See Appendix C.1 in Parameter Space Noise for Exploration, Plappert et al., 2017
# for detailed explanation.
update_param_noise_threshold = \
-np.log(1. - self.exploration.value(self.num_timesteps) +
self.exploration.value(self.num_timesteps) / float(self.env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
with self.sess.as_default():
####################################################
# MODIFICATION:
# Rename variable from original, since it's now
# going to come back as an array due to the
# modified build_act function being used to
# construct everything.
action_arr = self.act(np.array(obs)[None],
update_eps=update_eps,
**kwargs)[0]
####################################################
# ORIGINAL:
# action = self.act(np.array(obs)[None], update_eps=update_eps, **kwargs)[0]
########################################################
# MODIFICATION:
# Get the best action that has not yet been taken this
# episode.
action = \
action_arr[np.argmin(np.isin(action_arr, action_list))]
# Add this action to the list.
action_list.append(action)
########################################################
env_action = action
reset = False
new_obs, rew, done, info = self.env.step(env_action)
# Store transition in the replay buffer.
self.replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
if writer is not None:
ep_rew = np.array([rew]).reshape((1, -1))
ep_done = np.array([done]).reshape((1, -1))
total_episode_reward_logger(self.episode_reward, ep_rew,
ep_done, writer,
self.num_timesteps)
episode_rewards[-1] += rew
if done:
####################################################
# MODIFICATION:
# Clear the list.
action_list.clear()
####################################################
maybe_is_success = info.get('is_success')
if maybe_is_success is not None:
episode_successes.append(float(maybe_is_success))
if not isinstance(self.env, VecEnv):
obs = self.env.reset()
episode_rewards.append(0.0)
reset = True
# Do not train if the warmup phase is not over
# or if there are not enough samples in the replay buffer
can_sample = self.replay_buffer.can_sample(self.batch_size)
if can_sample and self.num_timesteps > self.learning_starts \
and self.num_timesteps % self.train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
# pytype:disable=bad-unpacking
if self.prioritized_replay:
assert self.beta_schedule is not None, \
"BUG: should be LinearSchedule when self.prioritized_replay True"
experience = self.replay_buffer.sample(
self.batch_size,
beta=self.beta_schedule.value(self.num_timesteps))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = self.replay_buffer.sample(
self.batch_size)
weights, batch_idxes = np.ones_like(rewards), None
# pytype:enable=bad-unpacking
if writer is not None:
# run loss backprop with summary, but once every 100 steps save the metadata
# (memory, compute time, ...)
if (1 + self.num_timesteps) % 100 == 0:
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary, td_errors = self._train_step(
obses_t,
actions,
rewards,
obses_tp1,
obses_tp1,
dones,
weights,
sess=self.sess,
options=run_options,
run_metadata=run_metadata)
writer.add_run_metadata(
run_metadata, 'step%d' % self.num_timesteps)
else:
summary, td_errors = self._train_step(
obses_t,
actions,
rewards,
obses_tp1,
obses_tp1,
dones,
weights,
sess=self.sess)
writer.add_summary(summary, self.num_timesteps)
else:
_, td_errors = self._train_step(obses_t,
actions,
rewards,
obses_tp1,
obses_tp1,
dones,
weights,
sess=self.sess)
if self.prioritized_replay:
new_priorities = np.abs(
td_errors) + self.prioritized_replay_eps
assert isinstance(self.replay_buffer,
PrioritizedReplayBuffer)
self.replay_buffer.update_priorities(
batch_idxes, new_priorities)
if can_sample and self.num_timesteps > self.learning_starts and \
self.num_timesteps % self.target_network_update_freq == 0:
# Update target network periodically.
self.update_target(sess=self.sess)
if len(episode_rewards[-101:-1]) == 0:
mean_100ep_reward = -np.inf
else:
mean_100ep_reward = round(
float(np.mean(episode_rewards[-101:-1])), 1)
num_episodes = len(episode_rewards)
if self.verbose >= 1 and done and log_interval is not None and len(
episode_rewards) % log_interval == 0:
logger.record_tabular("steps", self.num_timesteps)
logger.record_tabular("episodes", num_episodes)
if len(episode_successes) > 0:
logger.logkv("success rate",
np.mean(episode_successes[-100:]))
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward)
logger.record_tabular(
"% time spent exploring",
int(100 * self.exploration.value(self.num_timesteps)))
logger.dump_tabular()
self.num_timesteps += 1
return self
class DQN(OffPolicyRLModel):
"""
The DQN model class.
DQN paper: https://arxiv.org/abs/1312.5602
Dueling DQN: https://arxiv.org/abs/1511.06581
Double-Q Learning: https://arxiv.org/abs/1509.06461
Prioritized Experience Replay: https://arxiv.org/abs/1511.05952
:param policy: (DQNPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, LnMlpPolicy, ...)
:param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
:param gamma: (float) discount factor
:param learning_rate: (float) learning rate for adam optimizer
:param buffer_size: (int) size of the replay buffer
:param exploration_fraction: (float) fraction of entire training period over which the exploration rate is
annealed
:param exploration_final_eps: (float) final value of random action probability
:param exploration_initial_eps: (float) initial value of random action probability
:param train_freq: (int) update the model every `train_freq` steps. set to None to disable printing
:param batch_size: (int) size of a batched sampled from replay buffer for training
:param double_q: (bool) Whether to enable Double-Q learning or not.
:param learning_starts: (int) how many steps of the model to collect transitions for before learning starts
:param target_network_update_freq: (int) update the target network every `target_network_update_freq` steps.
:param prioritized_replay: (bool) if True prioritized replay buffer will be used.
:param prioritized_replay_alpha: (float)alpha parameter for prioritized replay buffer.
It determines how much prioritization is used, with alpha=0 corresponding to the uniform case.
:param prioritized_replay_beta0: (float) initial value of beta for prioritized replay buffer
:param prioritized_replay_beta_iters: (int) number of iterations over which beta will be annealed from initial
value to 1.0. If set to None equals to max_timesteps.
:param prioritized_replay_eps: (float) epsilon to add to the TD errors when updating priorities.
:param param_noise: (bool) Whether or not to apply noise to the parameters of the policy.
:param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
:param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
:param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
:param full_tensorboard_log: (bool) enable additional logging when using tensorboard
WARNING: this logging can take a lot of space quickly
:param seed: (int) Seed for the pseudo-random generators (python, numpy, tensorflow).
If None (default), use random seed. Note that if you want completely deterministic
results, you must set `n_cpu_tf_sess` to 1.
:param n_cpu_tf_sess: (int) The number of threads for TensorFlow operations
If None, the number of cpu of the current machine will be used.
"""
def __init__(self, policy, env, gamma=0.99, learning_rate=5e-4, buffer_size=50000, exploration_fraction=0.1,
exploration_final_eps=0.02, exploration_initial_eps=1.0, train_freq=1, batch_size=32, double_q=True,
learning_starts=1000, target_network_update_freq=500, prioritized_replay=False,
prioritized_replay_alpha=0.6, prioritized_replay_beta0=0.4, prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6, param_noise=False,
n_cpu_tf_sess=None, verbose=0, tensorboard_log=None,
_init_setup_model=True, policy_kwargs=None, full_tensorboard_log=False, seed=None,
use_rmsprop=False, rmsprop_alpha=0.95, rmsprop_epsilon=0.01, exploration_offset=0):
# TODO: replay_buffer refactoring
super(DQN, self).__init__(policy=policy, env=env, replay_buffer=None, verbose=verbose, policy_base=DQNPolicy,
requires_vec_env=False, policy_kwargs=policy_kwargs, seed=seed, n_cpu_tf_sess=n_cpu_tf_sess)
self.param_noise = param_noise
self.learning_starts = learning_starts
self.train_freq = train_freq
self.prioritized_replay = prioritized_replay
self.prioritized_replay_eps = prioritized_replay_eps
self.batch_size = batch_size
self.target_network_update_freq = target_network_update_freq
self.prioritized_replay_alpha = prioritized_replay_alpha
self.prioritized_replay_beta0 = prioritized_replay_beta0
self.prioritized_replay_beta_iters = prioritized_replay_beta_iters
self.exploration_final_eps = exploration_final_eps
self.exploration_initial_eps = exploration_initial_eps
self.exploration_fraction = exploration_fraction
self.buffer_size = buffer_size
self.learning_rate = learning_rate
self.gamma = gamma
self.tensorboard_log = tensorboard_log
self.full_tensorboard_log = full_tensorboard_log
self.double_q = double_q
self.use_rmsprop = use_rmsprop
self.rmsprop_alpha = rmsprop_alpha
self.rmsprop_epsilon = rmsprop_epsilon
self.exploration_offset = exploration_offset
self.graph = None
self.sess = None
self._train_step = None
self.step_model = None
self.update_target = None
self.act = None
self.proba_step = None
self.replay_buffer = None
self.beta_schedule = None
self.exploration = None
self.params = None
self.summary = None
self.episode_reward = None
if _init_setup_model:
self.setup_model()
def _get_pretrain_placeholders(self):
policy = self.step_model
return policy.obs_ph, tf.placeholder(tf.int32, [None]), policy.q_values
def setup_model(self):
with SetVerbosity(self.verbose):
assert not isinstance(self.action_space, gym.spaces.Box), \
"Error: DQN cannot output a gym.spaces.Box action space."
# If the policy is wrap in functool.partial (e.g. to disable dueling)
# unwrap it to check the class type
if isinstance(self.policy, partial):
test_policy = self.policy.func
else:
test_policy = self.policy
assert issubclass(test_policy, DQNPolicy), "Error: the input policy for the DQN model must be " \
"an instance of DQNPolicy."
self.graph = tf.Graph()
with self.graph.as_default():
self.set_random_seed(self.seed)
self.sess = tf_util.make_session(num_cpu=self.n_cpu_tf_sess, graph=self.graph)
if self.use_rmsprop:
optimizer = tf.train.RMSPropOptimizer(
learning_rate=self.learning_rate, decay=self.rmsprop_alpha, epsilon=self.rmsprop_epsilon,
centered=True
)
else:
optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
self.act, self._train_step, self.update_target, self.step_model = build_train(
q_func=partial(self.policy, **self.policy_kwargs),
ob_space=self.observation_space,
ac_space=self.action_space,
optimizer=optimizer,
gamma=self.gamma,
grad_norm_clipping=10,
param_noise=self.param_noise,
sess=self.sess,
full_tensorboard_log=self.full_tensorboard_log,
double_q=self.double_q
)
self.proba_step = self.step_model.proba_step
self.params = tf_util.get_trainable_vars("deepq")
# Initialize the parameters and copy them to the target network.
tf_util.initialize(self.sess)
self.update_target(sess=self.sess)
self.summary = tf.summary.merge_all()
def learn(self, total_timesteps, callback=None, log_interval=100, tb_log_name="DQN",
reset_num_timesteps=True, replay_wrapper=None):
new_tb_log = self._init_num_timesteps(reset_num_timesteps)
with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \
as writer:
self._setup_learn()
# Create the replay buffer
if self.prioritized_replay:
self.replay_buffer = PrioritizedReplayBuffer(self.buffer_size, alpha=self.prioritized_replay_alpha)
if self.prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
else:
prioritized_replay_beta_iters = self.prioritized_replay_beta_iters
self.beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=self.prioritized_replay_beta0,
final_p=1.0)
else:
self.replay_buffer = ReplayBuffer(self.buffer_size)
self.beta_schedule = None
if replay_wrapper is not None:
assert not self.prioritized_replay, "Prioritized replay buffer is not supported by HER"
self.replay_buffer = replay_wrapper(self.replay_buffer)
# Create the schedule for exploration starting from 1.
self.exploration = LinearSchedule(schedule_timesteps=int(self.exploration_fraction * total_timesteps),
initial_p=self.exploration_initial_eps,
final_p=self.exploration_final_eps)
episode_rewards = [0.0]
episode_successes = []
obs = self.env.reset()
reset = True
self.episode_reward = np.zeros((1,))
for _ in range(total_timesteps):
if callback is not None:
# Only stop training if return value is False, not when it is None. This is for backwards
# compatibility with callbacks that have no return statement.
if callback(locals(), globals()) is False:
break
# Take action and update exploration to the newest value
kwargs = {}
if not self.param_noise:
update_eps = self.exploration.value(self.num_timesteps - self.exploration_offset)
update_param_noise_threshold = 0.
else:
update_eps = 0.
# Compute the threshold such that the KL divergence between perturbed and non-perturbed
# policy is comparable to eps-greedy exploration with eps = exploration.value(t).
# See Appendix C.1 in Parameter Space Noise for Exploration, Plappert et al., 2017
# for detailed explanation.
update_param_noise_threshold = \
-np.log(1. - self.exploration.value(self.num_timesteps - self.exploration_offset) +
self.exploration.value(self.num_timesteps - self.exploration_offset) / float(self.env.action_space.n))
kwargs['reset'] = reset
kwargs['update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
with self.sess.as_default():
action = self.act(np.array(obs)[None], update_eps=update_eps, **kwargs)[0]
env_action = action
reset = False
new_obs, rew, done, info = self.env.step(env_action)
# Store transition in the replay buffer.
self.replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
if writer is not None:
ep_rew = np.array([rew]).reshape((1, -1))
ep_done = np.array([done]).reshape((1, -1))
self.episode_reward = total_episode_reward_logger(self.episode_reward, ep_rew, ep_done, writer,
self.num_timesteps)
episode_rewards[-1] += rew
if done:
maybe_is_success = info.get('is_success')
if maybe_is_success is not None:
episode_successes.append(float(maybe_is_success))
if not isinstance(self.env, VecEnv):
obs = self.env.reset()
episode_rewards.append(0.0)
reset = True
# Do not train if the warmup phase is not over
# or if there are not enough samples in the replay buffer
can_sample = self.replay_buffer.can_sample(self.batch_size)
if can_sample and self.num_timesteps > self.learning_starts \
and self.num_timesteps % self.train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
# pytype:disable=bad-unpacking
if self.prioritized_replay:
assert self.beta_schedule is not None, \
"BUG: should be LinearSchedule when self.prioritized_replay True"
experience = self.replay_buffer.sample(self.batch_size,
beta=self.beta_schedule.value(self.num_timesteps))
(obses_t, actions, rewards, obses_tp1, dones, weights, batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = self.replay_buffer.sample(self.batch_size)
weights, batch_idxes = np.ones_like(rewards), None
# pytype:enable=bad-unpacking
if writer is not None:
# run loss backprop with summary, but once every 100 steps save the metadata
# (memory, compute time, ...)
if (1 + self.num_timesteps) % 100 == 0:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary, td_errors = self._train_step(obses_t, actions, rewards, obses_tp1, obses_tp1,
dones, weights, sess=self.sess, options=run_options,
run_metadata=run_metadata)
writer.add_run_metadata(run_metadata, 'step%d' % self.num_timesteps)
else:
summary, td_errors = self._train_step(obses_t, actions, rewards, obses_tp1, obses_tp1,
dones, weights, sess=self.sess)
writer.add_summary(summary, self.num_timesteps)
else:
_, td_errors = self._train_step(obses_t, actions, rewards, obses_tp1, obses_tp1, dones, weights,
sess=self.sess)
if self.prioritized_replay:
new_priorities = np.abs(td_errors) + self.prioritized_replay_eps
assert isinstance(self.replay_buffer, PrioritizedReplayBuffer)
self.replay_buffer.update_priorities(batch_idxes, new_priorities)
if can_sample and self.num_timesteps > self.learning_starts and \
self.num_timesteps % self.target_network_update_freq == 0:
# Update target network periodically.
self.update_target(sess=self.sess)
if len(episode_rewards[-101:-1]) == 0:
mean_100ep_reward = -np.inf
else:
mean_100ep_reward = round(float(np.mean(episode_rewards[-101:-1])), 1)
num_episodes = len(episode_rewards)
if self.verbose >= 1 and done and log_interval is not None and len(episode_rewards) % log_interval == 0:
logger.record_tabular("steps", self.num_timesteps)
logger.record_tabular("episodes", num_episodes)
if len(episode_successes) > 0:
logger.logkv("success rate", np.mean(episode_successes[-100:]))
logger.record_tabular("mean 100 episode reward", mean_100ep_reward)
logger.record_tabular("% time spent exploring",
int(100 * self.exploration.value(self.num_timesteps - self.exploration_offset)))
logger.dump_tabular()
self.num_timesteps += 1
return episode_rewards
def predict(self, observation, state=None, mask=None, deterministic=True):
observation = np.array(observation)
vectorized_env = self._is_vectorized_observation(observation, self.observation_space)
observation = observation.reshape((-1,) + self.observation_space.shape)
with self.sess.as_default():
actions, _, _ = self.step_model.step(observation, deterministic=deterministic)
if not vectorized_env:
actions = actions[0]
return actions, None
def action_probability(self, observation, state=None, mask=None, actions=None, logp=False):
observation = np.array(observation)
vectorized_env = self._is_vectorized_observation(observation, self.observation_space)
observation = observation.reshape((-1,) + self.observation_space.shape)
actions_proba = self.proba_step(observation, state, mask)
if actions is not None: # comparing the action distribution, to given actions
actions = np.array([actions])
assert isinstance(self.action_space, gym.spaces.Discrete)
actions = actions.reshape((-1,))
assert observation.shape[0] == actions.shape[0], "Error: batch sizes differ for actions and observations."
actions_proba = actions_proba[np.arange(actions.shape[0]), actions]
# normalize action proba shape
actions_proba = actions_proba.reshape((-1, 1))
if logp:
actions_proba = np.log(actions_proba)
if not vectorized_env:
if state is not None:
raise ValueError("Error: The environment must be vectorized when using recurrent policies.")
actions_proba = actions_proba[0]
return actions_proba
def get_parameter_list(self):
return self.params
def save(self, save_path, cloudpickle=False):
# params
data = {
"double_q": self.double_q,
"param_noise": self.param_noise,
"learning_starts": self.learning_starts,
"train_freq": self.train_freq,
"prioritized_replay": self.prioritized_replay,
"prioritized_replay_eps": self.prioritized_replay_eps,
"batch_size": self.batch_size,
"target_network_update_freq": self.target_network_update_freq,
"prioritized_replay_alpha": self.prioritized_replay_alpha,
"prioritized_replay_beta0": self.prioritized_replay_beta0,
"prioritized_replay_beta_iters": self.prioritized_replay_beta_iters,
"exploration_final_eps": self.exploration_final_eps,
"exploration_fraction": self.exploration_fraction,
"learning_rate": self.learning_rate,
"gamma": self.gamma,
"verbose": self.verbose,
"observation_space": self.observation_space,
"action_space": self.action_space,
"policy": self.policy,
"n_envs": self.n_envs,
"n_cpu_tf_sess": self.n_cpu_tf_sess,
"seed": self.seed,
"_vectorize_action": self._vectorize_action,
"policy_kwargs": self.policy_kwargs
}
params_to_save = self.get_parameters()
self._save_to_file(save_path, data=data, params=params_to_save, cloudpickle=cloudpickle)
class OurDDPG(OffPolicyRLModel):
def __init__(self,
policy,
env,
seed=0,
eval_env=None,
eval_freq=5000,
gamma=0.99,
tau=0.005,
action_noise=None,
normalize_observations=False,
normalize_returns=False,
observation_range=(-np.inf, np.inf),
return_range=(-np.inf, np.inf),
reward_scale=1.,
critic_l2_reg=0.,
clip_norm=None,
actor_lr=1e-3,
critic_lr=1e-3,
buffer_size=1e6,
batch_size=128,
verbose=0,
policy_kwargs=None,
tensorboard_log=None,
full_tensorboard_log=False,
_init_setup_model=True,
ro=True,
sample_number=128,
adjust_lr=False):
super(OurDDPG, self).__init__(policy=policy,
env=env,
replay_buffer=None,
verbose=verbose,
policy_base=DDPGPolicy,
requires_vec_env=False,
policy_kwargs=policy_kwargs)
# Parameters.
self.seed = seed
self.gamma = gamma
self.tau = tau
self.ro = ro
self.sample_number = sample_number
self.eval_freq = eval_freq
self.adjust_lr = adjust_lr
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.return_range = return_range
self.observation_range = observation_range
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.reward_scale = reward_scale
self.batch_size = batch_size
self.critic_l2_reg = critic_l2_reg
self.eval_env = eval_env
self.buffer_size = buffer_size
self.tensorboard_log = tensorboard_log
self.full_tensorboard_log = full_tensorboard_log
# init
self.graph = None
self.stats_sample = None
self.replay_buffer = None
self.policy_tf = None
self.target_init_updates = None
self.target_soft_updates = None
self.critic_loss = None
self.critic_optimizer = None
self.critic_optimize_op = None
self.sess = None
self.stats_ops = None
self.stats_names = None
self.perturbed_actor_tf = None
self.perturb_policy_ops = None
self.perturb_adaptive_policy_ops = None
self.adaptive_policy_distance = None
self.actor_loss = None
self.actor_optimizer = None
self.actor_optimize_op = None
self.old_std = None
self.old_mean = None
self.renormalize_q_outputs_op = None
self.obs_rms = None
self.ret_rms = None
self.target_policy = None
self.actor_tf = None
self.critic_tf = None
self.critic_with_actor_tf = None
self.critic_with_actor_tf = None
self.target_q = None
self.obs_train_ph = None
self.action_train_ph = None
self.obs_target = None
self.action_target = None
self.obs_noise = None
self.action_noise_ph = None
self.obs_adapt_noise = None
self.action_adapt_noise = None
self.terminals1 = None
self.rewards = None
self.critic_target = None
self.param_noise_stddev = None
self.param_noise_actor = None
self.adaptive_param_noise_actor = None
self.params = None
self.summary = None
self.episode_reward = None
self.tb_seen_steps = None
self.target_params = None
self.obs_rms_params = None
self.ret_rms_params = None
# Randomized Optimization
self.augmented_obs0 = None
self.augmented_action_raw = None
self.augmented_action = None
self.augmented_critic_with_actor_tf = None
self.reward_summary = None
self.actor_loss_summary = None
self.critic_loss_summary = None
self.obs_summary = None
if _init_setup_model:
self.setup_model()
def _get_pretrain_placeholders(self):
return NotImplementedError
# policy = self.policy_tf
# # Rescale
# deterministic_action = self.actor_tf * np.abs(self.action_space.low)
# return policy.obs_ph, self.actions, deterministic_action
def setup_model(self):
with SetVerbosity(self.verbose):
assert isinstance(self.action_space, gym.spaces.Box), \
"Error: DDPG cannot output a {} action space, only spaces.Box is supported.".format(self.action_space)
assert issubclass(self.policy, DDPGPolicy), "Error: the input policy for the DDPG model must be " \
"an instance of DDPGPolicy."
self.graph = tf.Graph()
with self.graph.as_default():
self._setup_learn(self.seed)
# self.sess = tf_util.single_threaded_session(graph=self.graph)
self.sess = tf_util.make_session()
self.replay_buffer = ReplayBuffer(self.buffer_size)
with tf.variable_scope("input", reuse=False):
# Observation normalization.
# if self.normalize_observations:
# with tf.variable_scope('obs_rms'):
# self.obs_rms = RunningMeanStd(shape=self.observation_space.shape)
# else:
# self.obs_rms = None
# Return normalization.
# if self.normalize_returns:
# with tf.variable_scope('ret_rms'):
# self.ret_rms = RunningMeanStd()
# else:
# self.ret_rms = None
self.policy_tf = self.policy(self.sess,
self.observation_space,
self.action_space, 1, 1, None,
**self.policy_kwargs)
# Create target networks.
self.target_policy = self.policy(self.sess,
self.observation_space,
self.action_space, 1, 1,
None,
**self.policy_kwargs)
self.obs_target = self.target_policy.obs_ph
self.action_target = self.target_policy.action_ph
# normalized_obs0 = tf.clip_by_value(normalize(self.policy_tf.processed_obs, self.obs_rms),
# self.observation_range[0], self.observation_range[1])
# normalized_obs1 = tf.clip_by_value(normalize(self.target_policy.processed_obs, self.obs_rms),
# self.observation_range[0], self.observation_range[1])
# Inputs.
self.obs_train_ph = self.policy_tf.obs_ph
self.action_train_ph = self.policy_tf.action_ph
self.terminals1 = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rewards = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.critic_target = tf.placeholder(tf.float32,
shape=(None, 1),
name='critic_target')
# Create networks and core TF parts that are shared across setup parts.
with tf.variable_scope("model", reuse=False):
self.actor_tf = self.policy_tf.make_actor(
self.policy_tf.processed_obs)
self.critic_tf = self.policy_tf.make_critic(
self.policy_tf.processed_obs, self.action_train_ph)
self.critic_with_actor_tf = self.policy_tf.make_critic(
self.policy_tf.processed_obs,
self.actor_tf,
reuse=True)
if self.ro:
def tf_repeat(tensor_to_repeat, repeat_num):
tiled = tf.tile(tensor_to_repeat, [1, repeat_num])
repeated = tf.reshape(
tiled,
shape=[
self.batch_size * repeat_num,
tensor_to_repeat.shape[1]
])
return repeated
self.augmented_obs0 = tf_repeat(
self.policy_tf.processed_obs, self.sample_number)
self.augmented_action_raw = tf_repeat(
self.actor_tf, self.sample_number)
noises = []
for b_index in range(self.batch_size):
noises.append(
tf.random_uniform((self.sample_number - 1, ) +
self.action_space.shape,
-0.1, 0.1))
noises.append(
tf.zeros((1, ) + self.action_space.shape))
noises = tf.concat(noises, axis=0)
self.augmented_action = self.augmented_action_raw + noises
self.augmented_action = tf.clip_by_value(
self.augmented_action, -1, 1)
self.augmented_critic_with_actor_tf = self.policy_tf.make_critic(
self.augmented_obs0,
self.augmented_action,
reuse=True)[:, 0]
with tf.variable_scope("target", reuse=False):
critic_target = \
self.target_policy.make_critic(self.target_policy.processed_obs,
self.target_policy.make_actor(self.target_policy.processed_obs))
with tf.variable_scope("loss", reuse=False):
# self.critic_tf = denormalize(
# tf.clip_by_value(self.critic_tf, self.return_range[0], self.return_range[1]),
# self.ret_rms)
#
# self.critic_with_actor_tf = denormalize(
# tf.clip_by_value(self.critic_with_actor_tf,
# self.return_range[0], self.return_range[1]),
# self.ret_rms)
#
# q_obs1 = denormalize(critic_target, self.ret_rms)
self.target_q = self.rewards + (
1. - self.terminals1) * self.gamma * critic_target
# tf.summary.scalar('critic_target', tf.reduce_mean(self.critic_target))
if self.full_tensorboard_log:
tf.summary.histogram('critic_target',
self.critic_target)
# Set up parts.
self._setup_stats()
self._setup_target_network_updates()
with tf.variable_scope("input_info", reuse=False):
self.reward_summary = tf.summary.scalar(
'rewards', tf.reduce_mean(self.rewards))
self.obs_summary = tf.summary.scalar(
'obs', tf.reduce_mean(self.obs_train_ph))
if self.full_tensorboard_log:
tf.summary.histogram('rewards', self.rewards)
if len(self.observation_space.shape
) == 3 and self.observation_space.shape[0] in [
1, 3, 4
]:
tf.summary.image('observation', self.obs_train_ph)
else:
tf.summary.histogram('observation',
self.obs_train_ph)
with tf.variable_scope("Adam_mpi", reuse=False):
self._setup_actor_optimizer()
self._setup_critic_optimizer()
self.actor_loss_summary = tf.summary.scalar(
'actor_loss', self.actor_loss)
self.critic_loss_summary = tf.summary.scalar(
'critic_loss', self.critic_loss)
self.params = tf_util.get_trainable_vars("model")
self.target_params = tf_util.get_trainable_vars("target")
self.obs_rms_params = [
var for var in tf.global_variables()
if "obs_rms" in var.name
]
self.ret_rms_params = [
var for var in tf.global_variables()
if "ret_rms" in var.name
]
with self.sess.as_default():
self._initialize(self.sess)
# self.summary = tf.summary.merge_all()
def _setup_target_network_updates(self):
"""
set the target update operations
"""
init_updates, soft_updates = get_target_updates(
tf_util.get_trainable_vars('model/'),
tf_util.get_trainable_vars('target/'), self.tau, self.verbose)
self.target_init_updates = init_updates
self.target_soft_updates = soft_updates
def _setup_actor_optimizer(self):
"""
setup the optimizer for the actor
"""
if self.verbose >= 2:
logger.info('setting up actor optimizer')
if self.ro:
split_group_action_raw = tf.split(self.augmented_action_raw,
self.batch_size,
axis=0)
split_group_action = tf.split(self.augmented_action,
self.batch_size,
axis=0)
split_group_q = tf.split(self.augmented_critic_with_actor_tf,
self.batch_size,
axis=0)
self.actor_loss = 0
q_stds = []
for idx in range(self.batch_size):
# softmax = tf.nn.softmax(split_group_q[idx] -
# tf.reduce_max(split_group_q[idx], axis=0, keepdims=True), axis=0)
# self.actor_loss = self.actor_loss + tf.reduce_sum(
# tf.reduce_sum(tf.square(split_group_action_raw[idx] -
# tf.stop_gradient(split_group_action[idx])),
# axis=1)
# * tf.stop_gradient(softmax))
max_index = tf.argmax(split_group_q[idx], axis=0)
q_std = tf.math.reduce_std(split_group_q[idx]) * 20
target_action = split_group_action[idx][max_index, :]
if self.adjust_lr:
self.actor_loss = self.actor_loss + \
tf.reduce_mean(tf.square(self.actor_tf[idx, :] - tf.stop_gradient(target_action))) \
/ tf.stop_gradient(q_std)
else:
self.actor_loss = self.actor_loss + \
tf.reduce_mean(tf.square(self.actor_tf[idx, :] - tf.stop_gradient(target_action)))
q_stds.append(q_std)
# tf.summary.histogram("q_std", tf.stack(q_stds, axis=0))
else:
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [
var.get_shape().as_list()
for var in tf_util.get_trainable_vars('model/pi/')
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
if self.verbose >= 2:
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
# self.actor_grads = tf_util.flatgrad(self.actor_loss, tf_util.get_trainable_vars('model/pi/'),
# clip_norm=self.clip_norm)
# self.actor_optimizer = MpiAdam(var_list=tf_util.get_trainable_vars('model/pi/'), beta1=0.9, beta2=0.999,
# epsilon=1e-08)
self.actor_optimizer = tf.train.AdamOptimizer(
learning_rate=self.actor_lr)
self.actor_gradients = self.actor_optimizer.compute_gradients(
self.actor_loss, var_list=tf_util.get_trainable_vars("model/pi/"))
hist_summary = []
for gradient, variable in self.actor_gradients:
if gradient is not None:
hist_summary.append(
tf.summary.histogram("gradients/" + variable.name,
gradient))
hist_summary.append(
tf.summary.histogram("variables/" + variable.name,
variable))
self.actor_gradient_summary = tf.summary.merge(hist_summary)
self.actor_optimize_op = self.actor_optimizer.apply_gradients(
self.actor_gradients)
# self.actor_optimize_op = self.actor_optimizer.minimize(self.actor_loss,
# var_list=tf_util.get_trainable_vars("model/pi/"))
def _setup_critic_optimizer(self):
"""
setup the optimizer for the critic
"""
if self.verbose >= 2:
logger.info('setting up critic optimizer')
# normalized_critic_target_tf = tf.clip_by_value(normalize(self.critic_target, self.ret_rms),
# self.return_range[0], self.return_range[1])
# self.critic_loss = tf.reduce_mean(tf.square(self.critic_tf - normalized_critic_target_tf))
self.critic_loss = tf.reduce_mean(
tf.square(self.critic_tf - self.critic_target))
if self.critic_l2_reg > 0.:
critic_reg_vars = [
var for var in tf_util.get_trainable_vars('model/qf/')
if 'bias' not in var.name and 'qf_output' not in var.name
and 'b' not in var.name
]
if self.verbose >= 2:
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars)
self.critic_loss += critic_reg
critic_shapes = [
var.get_shape().as_list()
for var in tf_util.get_trainable_vars('model/qf/')
]
critic_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
if self.verbose >= 2:
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
# self.critic_grads = tf_util.flatgrad(self.critic_loss, tf_util.get_trainable_vars('model/qf/'),
# clip_norm=self.clip_norm)
# self.critic_optimizer = MpiAdam(var_list=tf_util.get_trainable_vars('model/qf/'), beta1=0.9, beta2=0.999,
# epsilon=1e-08)
self.critic_optimizer = tf.train.AdamOptimizer(
learning_rate=self.critic_lr)
self.critic_optimize_op = self.critic_optimizer.minimize(
self.critic_loss, var_list=tf_util.get_trainable_vars("model/qf/"))
def _setup_stats(self):
"""
setup the running means and std of the inputs and outputs of the model
"""
ops = []
names = []
# if self.normalize_returns:
# ops += [self.ret_rms.mean, self.ret_rms.std]
# names += ['ret_rms_mean', 'ret_rms_std']
#
# if self.normalize_observations:
# ops += [tf.reduce_mean(self.obs_rms.mean), tf.reduce_mean(self.obs_rms.std)]
# names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
self.stats_ops = ops
self.stats_names = names
def _policy(self, obs, apply_noise=True, compute_q=True):
"""
Get the actions and critic output, from a given observation
:param obs: ([float] or [int]) the observation
:param apply_noise: (bool) enable the noise
:param compute_q: (bool) compute the critic output
:return: ([float], float) the action and critic value
"""
obs = np.array(obs).reshape((-1, ) + self.observation_space.shape)
feed_dict = {self.obs_train_ph: obs}
actor_tf = self.actor_tf
if compute_q:
action, q_value = self.sess.run(
[actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q_value = None
action = action.flatten()
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action.shape
action += noise
action = np.clip(action, -1, 1)
return action, q_value
def _store_transition(self, obs0, action, reward, obs1, terminal1):
"""
Store a transition in the replay buffer
:param obs0: ([float] or [int]) the last observation
:param action: ([float]) the action
:param reward: (float] the reward
:param obs1: ([float] or [int]) the current observation
:param terminal1: (bool) Whether the episode is over
"""
reward *= self.reward_scale
self.replay_buffer.add(obs0, action, reward, obs1, float(terminal1))
if self.normalize_observations:
self.obs_rms.update(np.array([obs0]))
def _train_step(self, step, writer, do_actor_update):
"""
run a step of training from batch
:param step: (int) the current step iteration
:param writer: (TensorFlow Summary.writer) the writer for tensorboard
:param log: (bool) whether or not to log to metadata
:return: (float, float) critic loss, actor loss
"""
# Get a batch
obs0, actions, rewards, obs1, terminals1 = self.replay_buffer.sample(
batch_size=self.batch_size)
# Reshape to match previous behavior and placeholder shape
rewards = rewards.reshape(-1, 1)
terminals1 = terminals1.reshape(-1, 1)
target_q = self.sess.run(self.target_q,
feed_dict={
self.obs_target: obs1,
self.rewards: rewards,
self.terminals1: terminals1
})
# Get all gradients and perform a synced update.
# ops = [self.actor_grads, self.actor_loss, self.critic_grads, self.critic_loss]
td_map = {
self.obs_train_ph: obs0,
# self.actions: actions,
self.action_train_ph: actions,
self.rewards: rewards,
self.critic_target: target_q,
}
critic_loss_summary, reward_summary, obs_summary, critic_loss, _ = \
self.sess.run([self.critic_loss_summary, self.reward_summary, self.obs_summary,
self.critic_loss, self.critic_optimize_op], td_map)
# self.critic_optimizer.update(critic_grads, learning_rate=self.critic_lr)
writer.add_summary(critic_loss_summary, step)
writer.add_summary(reward_summary, step)
writer.add_summary(obs_summary, step)
actor_loss = None
if do_actor_update:
actor_loss_summary, actor_gradient_summary, actor_loss, _ = \
self.sess.run([self.actor_loss_summary, self.actor_gradient_summary, self.actor_loss, self.actor_optimize_op], td_map)
# self.actor_optimizer.update(actor_grads, learning_rate=self.actor_lr)
writer.add_summary(actor_gradient_summary, step)
writer.add_summary(actor_loss_summary, step)
return critic_loss, actor_loss
def _initialize(self, sess):
"""
initialize the model parameters and optimizers
:param sess: (TensorFlow Session) the current TensorFlow session
"""
self.sess = sess
self.sess.run(tf.global_variables_initializer())
# self.actor_optimizer.sync()
# self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def _update_target_net(self):
"""
run target soft update operation
"""
self.sess.run(self.target_soft_updates)
def _get_stats(self):
"""
Get the mean and standard deviation of the model's inputs and outputs
:return: (dict) the means and stds
"""
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
obs0, actions, rewards, obs1, terminals1 = self.replay_buffer.sample(
batch_size=self.batch_size)
self.stats_sample = {
'obs0': obs0,
'actions': actions,
'rewards': rewards,
'obs1': obs1,
'terminals1': terminals1
}
# feed_dict = {
# self.actions: self.stats_sample['actions']
# }
feed_dict = {}
for placeholder in [self.action_train_ph, self.action_target]:
if placeholder is not None:
feed_dict[placeholder] = self.stats_sample['actions']
for placeholder in [self.obs_train_ph, self.obs_target]:
if placeholder is not None:
feed_dict[placeholder] = self.stats_sample['obs0']
values = self.sess.run(self.stats_ops, feed_dict=feed_dict)
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
return stats
def _reset(self):
"""
Reset internal state after an episode is complete.
"""
if self.action_noise is not None:
self.action_noise.reset()
def learn(self,
total_timesteps,
callback=None,
seed=None,
log_interval=None,
tb_log_name="DDPG",
reset_num_timesteps=True,
replay_wrapper=None):
new_tb_log = self._init_num_timesteps(reset_num_timesteps)
if replay_wrapper is not None:
self.replay_buffer = replay_wrapper(self.replay_buffer)
with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \
as writer:
# a list for tensorboard logging, to prevent logging with the same step number, if it already occured
self.tb_seen_steps = []
# rank = MPI.COMM_WORLD.Get_rank()
# we assume symmetric actions.
assert np.all(
np.abs(self.env.action_space.low) ==
self.env.action_space.high)
if self.verbose >= 2:
logger.log('Using agent with the following configuration:')
logger.log(str(self.__dict__.items()))
with self.sess.as_default(), self.graph.as_default():
# Prepare everything.
self._reset()
obs = self.env.reset()
eval_obs = None
if self.eval_env is not None:
eval_obs = self.eval_env.reset()
episode_rewards_deque = deque(maxlen=100)
eval_episode_rewards_deque = deque(maxlen=100)
self.episode_reward = np.zeros((1, ))
episode_successes = []
episode_rewards_all = []
episode_steps_all = []
episode_reward = 0.
episode_step = 0
total_steps = 0
step_since_eval = 0
total_episode_num = 0
start_time = time.time()
while True:
# Perform rollouts.
qs_this_rollout_period = []
actions_this_rollout_period = []
while True:
if total_steps >= total_timesteps:
return self
# Predict next action.
if total_steps <= 10000:
action = self.env.action_space.sample()
q_value = 0
else:
action, q_value = self._policy(obs,
apply_noise=True,
compute_q=True)
assert action.shape == self.env.action_space.shape
rescaled_action = action * np.abs(
self.action_space.low)
new_obs, reward, done, info = self.env.step(
rescaled_action)
if writer is not None:
ep_rew = np.array([reward]).reshape((1, -1))
ep_done = np.array([done]).reshape((1, -1))
self.episode_reward = total_episode_reward_logger(
self.episode_reward, ep_rew, ep_done, writer,
self.num_timesteps)
total_steps += 1
self.num_timesteps += 1
episode_reward += reward
episode_step += 1
step_since_eval += 1
# Book-keeping.
actions_this_rollout_period.append(action)
qs_this_rollout_period.append(q_value)
self._store_transition(obs, action, reward, new_obs,
done)
obs = new_obs
if done:
# Episode done.
episode_rewards_all.append(episode_reward)
episode_rewards_deque.append(episode_reward)
episode_steps_all.append(episode_step)
episode_reward = 0.
episode_step = 0
total_episode_num += 1
maybe_is_success = info.get('is_success')
if maybe_is_success is not None:
episode_successes.append(
float(maybe_is_success))
self._reset()
if not isinstance(self.env, VecEnv):
obs = self.env.reset()
break
# Train.
actor_losses_this_train_period = []
critic_losses_this_train_period = []
last_episode_step = int(episode_steps_all[-1])
for t_train in range(last_episode_step):
# Not enough samples in the replay buffer
if not self.replay_buffer.can_sample(self.batch_size):
break
# weird equation to deal with the fact the nb_train_steps will be different
# to nb_rollout_steps
step = total_steps - last_episode_step + t_train
critic_loss, actor_loss = self._train_step(
step, writer, do_actor_update=t_train % 2 == 0)
critic_losses_this_train_period.append(critic_loss)
if actor_loss:
actor_losses_this_train_period.append(actor_loss)
self._update_target_net()
# Evaluate.
eval_episode_rewards = []
eval_qs = []
if self.eval_env is not None and step_since_eval >= self.eval_freq:
step_since_eval %= self.eval_freq
eval_episode_reward = 0.
eval_episode = 0
while eval_episode < 10:
eval_action, eval_q = self._policy(
eval_obs, apply_noise=False, compute_q=True)
eval_obs, eval_r, eval_done, _ = self.eval_env.step(
eval_action * np.abs(self.action_space.low))
eval_episode_reward += eval_r
eval_qs.append(eval_q)
if eval_done:
if not isinstance(self.env, VecEnv):
eval_obs = self.eval_env.reset()
eval_episode_rewards.append(
eval_episode_reward)
eval_episode_rewards_deque.append(
eval_episode_reward)
eval_episode_reward = 0.
eval_episode += 1
if callback is not None:
# Only stop training if return value is False, not when it is None.
# This is for backwards compatibility with callbacks that have no return statement.
if callback(locals(), globals()) is False:
return self
# mpi_size = MPI.COMM_WORLD.Get_size()
# Log stats.
# XXX shouldn't call np.mean on variable length lists
duration = time.time() - start_time
stats = self._get_stats()
combined_stats = stats.copy()
combined_stats['rollout/return'] = episode_rewards_all[-1]
combined_stats['rollout/return_last_100'] = np.mean(
episode_rewards_deque)
combined_stats[
'rollout/episode_steps'] = episode_steps_all[-1]
combined_stats['debug/actions_mean'] = np.mean(
actions_this_rollout_period)
combined_stats['debug/actions_std'] = np.std(
actions_this_rollout_period)
combined_stats['debug/Q_mean'] = np.mean(
qs_this_rollout_period)
combined_stats['train/loss_actor'] = np.mean(
actor_losses_this_train_period)
combined_stats['train/loss_critic'] = np.mean(
critic_losses_this_train_period)
combined_stats['total/duration'] = duration
combined_stats['total/steps_per_second'] = float(
total_steps) / float(duration)
# Evaluation statistics.
if self.eval_env is not None and eval_episode_rewards:
combined_stats['eval/return'] = np.mean(
eval_episode_rewards)
combined_stats['eval/return_history'] = np.mean(
eval_episode_rewards_deque)
combined_stats['eval/Q'] = np.mean(eval_qs)
combined_stats['eval/episodes'] = len(
eval_episode_rewards)
def as_scalar(scalar):
"""
check and return the input if it is a scalar, otherwise raise ValueError
:param scalar: (Any) the object to check
:return: (Number) the scalar if x is a scalar
"""
if isinstance(scalar, np.ndarray):
assert scalar.size == 1
return scalar[0]
elif np.isscalar(scalar):
return scalar
else:
raise ValueError('expected scalar, got %s' %
scalar)
# combined_stats_sums = MPI.COMM_WORLD.allreduce(
# np.array([as_scalar(x) for x in combined_stats.values()]))
# combined_stats = {k: v / mpi_size for (k, v) in zip(combined_stats.keys(), combined_stats_sums)}
# Total statistics.
combined_stats['total/episodes'] = total_episode_num
combined_stats['total/steps'] = total_steps
for key in sorted(combined_stats.keys()):
logger.record_tabular(key, combined_stats[key])
if len(episode_successes) > 0:
logger.logkv("success rate",
np.mean(episode_successes[-100:]))
logger.dump_tabular()
logger.info('')
logdir = logger.get_dir()
# if rank == 0 and logdir:
# if hasattr(self.env, 'get_state'):
# with open(os.path.join(logdir, 'env_state.pkl'), 'wb') as file_handler:
# pickle.dump(self.env.get_state(), file_handler)
# if self.eval_env and hasattr(self.eval_env, 'get_state'):
# with open(os.path.join(logdir, 'eval_env_state.pkl'), 'wb') as file_handler:
# pickle.dump(self.eval_env.get_state(), file_handler)
def predict(self, observation, state=None, mask=None, deterministic=True):
observation = np.array(observation)
vectorized_env = self._is_vectorized_observation(
observation, self.observation_space)
observation = observation.reshape((-1, ) +
self.observation_space.shape)
actions, _, = self._policy(observation,
apply_noise=not deterministic,
compute_q=False)
actions = actions.reshape(
(-1, ) +
self.action_space.shape) # reshape to the correct action shape
actions = actions * np.abs(
self.action_space.low) # scale the output for the prediction
if not vectorized_env:
actions = actions[0]
return actions, None
def action_probability(self,
observation,
state=None,
mask=None,
actions=None):
observation = np.array(observation)
if actions is not None:
raise ValueError("Error: DDPG does not have action probabilities.")
# here there are no action probabilities, as DDPG does not use a probability distribution
warnings.warn(
"Warning: action probability is meaningless for DDPG. Returning None"
)
return None
def get_parameter_list(self):
return (self.params + self.target_params + self.obs_rms_params +
self.ret_rms_params)
def save(self, save_path):
data = {
"ro": self.ro,
"seed": self.seed,
"sample_number": self.sample_number,
"eval_freq": self.eval_freq,
"adjust_lr": self.adjust_lr,
"observation_space": self.observation_space,
"action_space": self.action_space,
"verbose": self.verbose,
"action_noise": self.action_noise,
"gamma": self.gamma,
"tau": self.tau,
"normalize_returns": self.normalize_returns,
"normalize_observations": self.normalize_observations,
"batch_size": self.batch_size,
"observation_range": self.observation_range,
"return_range": self.return_range,
"critic_l2_reg": self.critic_l2_reg,
"actor_lr": self.actor_lr,
"critic_lr": self.critic_lr,
"clip_norm": self.clip_norm,
"reward_scale": self.reward_scale,
"buffer_size": self.buffer_size,
"policy": self.policy,
"n_envs": self.n_envs,
"_vectorize_action": self._vectorize_action,
"policy_kwargs": self.policy_kwargs
}
params_to_save = self.get_parameters()
self._save_to_file(save_path, data=data, params=params_to_save)
@classmethod
def load(cls, load_path, env=None, **kwargs):
data, params = cls._load_from_file(load_path)
if 'policy_kwargs' in kwargs and kwargs['policy_kwargs'] != data[
'policy_kwargs']:
raise ValueError(
"The specified policy kwargs do not equal the stored policy kwargs. "
"Stored kwargs: {}, specified kwargs: {}".format(
data['policy_kwargs'], kwargs['policy_kwargs']))
model = cls(None, env, _init_setup_model=False)
model.__dict__.update(data)
model.__dict__.update(kwargs)
model.set_env(env)
model.setup_model()
# Patch for version < v2.6.0, duplicated keys where saved
if len(params) > len(model.get_parameter_list()):
n_params = len(model.params)
n_target_params = len(model.target_params)
n_normalisation_params = len(model.obs_rms_params) + len(
model.ret_rms_params)
# Check that the issue is the one from
# https://github.com/hill-a/stable-baselines/issues/363
assert len(params) == 2 * (n_params + n_target_params) + n_normalisation_params,\
"The number of parameter saved differs from the number of parameters"\
" that should be loaded: {}!={}".format(len(params), len(model.get_parameter_list()))
# Remove duplicates
params_ = params[:n_params + n_target_params]
if n_normalisation_params > 0:
params_ += params[-n_normalisation_params:]
params = params_
model.load_parameters(params)
return model
class MADQN(OffPolicyRLModel):
"""
The DQN model class.
DQN paper: https://arxiv.org/abs/1312.5602
Dueling DQN: https://arxiv.org/abs/1511.06581
Double-Q Learning: https://arxiv.org/abs/1509.06461
Prioritized Experience Replay: https://arxiv.org/abs/1511.05952
:param policy: (DQNPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, LnMlpPolicy, ...)
:param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
:param gamma: (float) discount factor
:param learning_rate: (float) learning rate for adam optimizer
:param buffer_size: (int) size of the replay buffer
:param exploration_fraction: (float) fraction of entire training period over which the exploration rate is
annealed
:param exploration_final_eps: (float) final value of random action probability
:param exploration_initial_eps: (float) initial value of random action probability
:param train_freq: (int) update the model every `train_freq` steps. set to None to disable printing
:param batch_size: (int) size of a batched sampled from replay buffer for training
:param double_q: (bool) Whether to enable Double-Q learning or not.
:param learning_starts: (int) how many steps of the model to collect transitions for before learning starts
:param target_network_update_freq: (int) update the target network every `target_network_update_freq` steps.
:param prioritized_replay: (bool) if True prioritized replay buffer will be used.
:param prioritized_replay_alpha: (float)alpha parameter for prioritized replay buffer.
It determines how much prioritization is used, with alpha=0 corresponding to the uniform case.
:param prioritized_replay_beta0: (float) initial value of beta for prioritized replay buffer
:param prioritized_replay_beta_iters: (int) number of iterations over which beta will be annealed from initial
value to 1.0. If set to None equals to max_timesteps.
:param prioritized_replay_eps: (float) epsilon to add to the TD errors when updating priorities.
:param param_noise: (bool) Whether or not to apply noise to the parameters of the policy.
:param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
:param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
:param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
:param full_tensorboard_log: (bool) enable additional logging when using tensorboard
WARNING: this logging can take a lot of space quickly
:param seed: (int) Seed for the pseudo-random generators (python, numpy, tensorflow).
If None (default), use random seed. Note that if you want completely deterministic
results, you must set `n_cpu_tf_sess` to 1.
:param n_cpu_tf_sess: (int) The number of threads for TensorFlow operations
If None, the number of cpu of the current machine will be used.
"""
def __init__(self,
policy,
env,
gamma=0.99,
learning_rate=5e-4,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
exploration_initial_eps=1.0,
train_freq=1,
batch_size=32,
double_q=True,
learning_starts=1000,
target_network_update_freq=500,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
n_cpu_tf_sess=None,
verbose=0,
tensorboard_log=None,
_init_setup_model=True,
policy_kwargs=None,
full_tensorboard_log=False,
seed=None,
num_agents=1): # MA-MOD
# TODO: replay_buffer refactoring
super(MADQN, self).__init__(policy=policy,
env=env,
replay_buffer=None,
verbose=verbose,
policy_base=DQNPolicy,
requires_vec_env=False,
policy_kwargs=policy_kwargs,
seed=seed,
n_cpu_tf_sess=n_cpu_tf_sess)
# print("POLICY TYPE", policy)
if self.observation_space:
obs_sp_low = self.observation_space.low[0, :]
obs_sp_high = self.observation_space.high[0, :]
self.observation_space = gym.spaces.Box(low=obs_sp_low,
high=obs_sp_high)
self.param_noise = param_noise
self.learning_starts = learning_starts
self.train_freq = train_freq
self.prioritized_replay = prioritized_replay
self.prioritized_replay_eps = prioritized_replay_eps
self.batch_size = batch_size
self.target_network_update_freq = target_network_update_freq
self.prioritized_replay_alpha = prioritized_replay_alpha
self.prioritized_replay_beta0 = prioritized_replay_beta0
self.prioritized_replay_beta_iters = prioritized_replay_beta_iters
self.exploration_final_eps = exploration_final_eps
self.exploration_initial_eps = exploration_initial_eps
self.exploration_fraction = exploration_fraction
self.buffer_size = buffer_size
self.learning_rate = learning_rate
self.gamma = gamma
self.tensorboard_log = tensorboard_log
self.full_tensorboard_log = full_tensorboard_log
self.double_q = double_q
self.num_agents = num_agents
self.graph = None
self.sess = None
self._train_step = [] # MA-MOD
self.step_model = [] # MA-MOD
self.update_target = [] # MA-MOD
self.act = [] # MA-MOD
self.proba_step = [] # MA-MOD
self.replay_buffer = None # TODO: Possibly try seperate replay buffer. If everything symmetric, OK for one.
# If you have the same Value function, its fine. If you have seperate functions, if you have one replay buffer, they learn from the same data.
self.beta_schedule = None
self.exploration = None
self.params = None
self.summary = None
if _init_setup_model:
self.setup_model()
def _get_pretrain_placeholders(self):
assert False, "MAKE SURE THIS FUNCTION ISNT CALLED"
policy = self.step_model
return policy.obs_ph, tf.placeholder(tf.int32, [None]), policy.q_values
def setup_model(self):
with SetVerbosity(self.verbose):
for i in range(self.num_agents):
assert not isinstance(self.action_space, gym.spaces.Box), \
"Error: DQN cannot output a gym.spaces.Box action space."
# If the policy is wrap in functool.partial (e.g. to disable dueling)
# unwrap it to check the class type
if isinstance(self.policy, partial):
test_policy = self.policy.func
else:
test_policy = self.policy
# print(test_policy.type)
assert issubclass(test_policy, DQNPolicy), "Error: the input policy for the DQN model must be " \
"an instance of DQNPolicy."
self.graph = tf.Graph()
with self.graph.as_default():
self.set_random_seed(self.seed)
self.sess = tf_util.make_session(num_cpu=self.n_cpu_tf_sess,
graph=self.graph)
self.params = []
print("AC SPC", self.action_space)
for i in range(self.num_agents):
with tf.variable_scope("agent" + str(i)):
optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate)
act, _train_step, update_target, step_model = build_train(
q_func=partial(self.policy, **self.policy_kwargs),
ob_space=self.observation_space,
ac_space=self.action_space,
optimizer=optimizer,
gamma=self.gamma,
grad_norm_clipping=10,
param_noise=self.param_noise,
sess=self.sess,
full_tensorboard_log=
False, #self.full_tensorboard_log,
double_q=self.double_q)
self.act.append(act)
self._train_step.append(_train_step)
self.step_model.append(step_model)
self.proba_step.append(step_model.proba_step)
self.update_target.append(update_target)
self.params.extend(
tf_util.get_trainable_vars("agent" + str(i) +
"/deepq"))
print(self.params)
# Initialize the parameters and copy them to the target network.
tf_util.initialize(
self.sess
) # TODO: copy this file, make two versions of the algorithm.
for i in range(self.num_agents):
self.update_target[i](
sess=self.sess
) # TODO: Not sure, seems like the best thing to do is try using each agents own target first.
# self.summary = tf.summary.merge_all()
def learn(self,
total_timesteps,
callback=None,
log_interval=100,
tb_log_name="DQN",
reset_num_timesteps=True,
replay_wrapper=None):
new_tb_log = self._init_num_timesteps(reset_num_timesteps)
# callback = self._init_callback(callback)
# with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \
# as writer:
self._setup_learn()
# Create the replay buffer
if self.prioritized_replay:
self.replay_buffer = PrioritizedReplayBuffer(
self.buffer_size, alpha=self.prioritized_replay_alpha)
if self.prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
else:
prioritized_replay_beta_iters = self.prioritized_replay_beta_iters
self.beta_schedule = LinearSchedule(
prioritized_replay_beta_iters,
initial_p=self.prioritized_replay_beta0,
final_p=1.0)
else:
self.replay_buffer = ReplayBuffer(self.buffer_size)
self.beta_schedule = None
if replay_wrapper is not None:
assert not self.prioritized_replay, "Prioritized replay buffer is not supported by HER"
self.replay_buffer = replay_wrapper(self.replay_buffer)
# Create the schedule for exploration starting from 1.
self.exploration = LinearSchedule(
schedule_timesteps=int(self.exploration_fraction *
total_timesteps),
initial_p=self.exploration_initial_eps,
final_p=self.exploration_final_eps)
episode_rewards = [[0.0] * self.num_agents] #MA-MOD
episode_successes = []
#callback.on_training_start(locals(), globals())
#callback.on_rollout_start()
reset = True
obs = self.env.reset()
for _ in range(total_timesteps):
# Take action and update exploration to the newest value
kwargs = {}
if not self.param_noise:
update_eps = self.exploration.value(self.num_timesteps)
update_param_noise_threshold = 0.
else:
update_eps = 0.
# Compute the threshold such that the KL divergence between perturbed and non-perturbed
# policy is comparable to eps-greedy exploration with eps = exploration.value(t).
# See Appendix C.1 in Parameter Space Noise for Exploration, Plappert et al., 2017
# for detailed explanation.
update_param_noise_threshold = \
-np.log(1. - self.exploration.value(self.num_timesteps) +
self.exploration.value(self.num_timesteps) / float(self.env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
with self.sess.as_default():
env_action = [] # MA-MOD
for i in range(self.num_agents
): # MA-MOD. This is fine for one policy.
action = self.act[i](
np.array(obs[i])[None],
update_eps=update_eps,
**kwargs
)[0] # TODO: Is this the correct way to get the correct agent obs?
env_action.append(action)
reset = False
new_obs, rew, done, info = self.env.step(
env_action
) # NOUPDATE - env.step should take a vector of actions
'''
Obs: x_me, x_opp --- agent 1. In env: x_1, x_2
Obs: x_me, x_opp -- agent 2. In env: x_2, x_1
Env: (n_agents, state_dim)
'''
self.num_timesteps += 1
# Stop training if return value is False
# if callback.on_step() is False:
# break
# Store transition in the replay buffer.
# Loop for replay buffer -- either separate or joined. obs[agent_index], action[agent_index], reward[agent_index]
# Joey: Does this look right to you?
# print(obs, action, rew, new_obs, done)
#print("obs",obs[0])
#print(action)
#print("ac", action[0])
#print("rew", rew[0])
#print("done", done[0])
for num_agent in range(self.num_agents):
self.replay_buffer.add(obs[num_agent], env_action[num_agent],
rew[num_agent], new_obs[num_agent],
float(done[num_agent]))
obs = new_obs
# if writer is not None:
# ep_rew = np.array([rew]).reshape((1, -1))
# ep_done = np.array([done]).reshape((1, -1))
# tf_util.total_episode_reward_logger(self.episode_reward, ep_rew, ep_done, writer,
# self.num_timesteps)
# TODO: current episode_rewards is a list, make it a list of lists where each list is the reward for each agent in all timesteps
# append the newest reward to the end of each list for each agent
for num_agent in range(self.num_agents): #MA-MOD
episode_rewards[-1][num_agent] += rew[num_agent]
if done.any():
maybe_is_success = info.get('is_success')
if maybe_is_success is not None:
episode_successes.append(float(maybe_is_success))
if not isinstance(self.env, VecEnv):
obs = self.env.reset()
episode_rewards.append([0.0] * self.num_agents)
reset = True
# Do not train if the warmup phase is not over
# or if there are not enough samples in the replay buffer
can_sample = self.replay_buffer.can_sample(self.batch_size)
if can_sample and self.num_timesteps > self.learning_starts \
and self.num_timesteps % self.train_freq == 0:
# callback.on_rollout_end()
for i in range(self.num_agents): # MA-MOD
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
# pytype:disable=bad-unpacking
if self.prioritized_replay:
assert self.beta_schedule is not None, \
"BUG: should be LinearSchedule when self.prioritized_replay True"
experience = self.replay_buffer.sample(
self.batch_size,
beta=self.beta_schedule.value(self.num_timesteps))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = self.replay_buffer.sample(
self.batch_size)
weights, batch_idxes = np.ones_like(rewards), None
# pytype:enable=bad-unpacking
# if writer is not None:
# # run loss backprop with summary, but once every 100 steps save the metadata
# # (memory, compute time, ...)
# if (1 + self.num_timesteps) % 100 == 0:
# run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
# run_metadata = tf.RunMetadata()
# summary, td_errors = self._train_step[i](obses_t, actions, rewards, obses_tp1, obses_tp1,
# dones, weights, sess=self.sess, options=run_options,
# run_metadata=run_metadata)
# writer.add_run_metadata(run_metadata, 'step%d_agent%d' % (self.num_timesteps, i))
# else:
# summary, td_errors = self._train_step[i](obses_t, actions, rewards, obses_tp1, obses_tp1,
# dones, weights, sess=self.sess)
# writer.add_summary(summary, self.num_timesteps)
# else:
td_errors = self._train_step[i](obses_t,
actions,
rewards,
obses_tp1,
obses_tp1,
dones,
weights,
sess=self.sess)
if self.prioritized_replay: # NOUPDATE - not inside main agent for loop
new_priorities = np.abs(
td_errors) + self.prioritized_replay_eps # NOUPDATE
assert isinstance(self.replay_buffer,
PrioritizedReplayBuffer)
self.replay_buffer.update_priorities(
batch_idxes, new_priorities)
# callback.on_rollout_start()
if can_sample and self.num_timesteps > self.learning_starts and \
self.num_timesteps % self.target_network_update_freq == 0:
# Update target network periodically.
for i in range(self.num_agents):
self.update_target[i](sess=self.sess) # MA-MOD
if len(episode_rewards[-101:-1]) == 0: # MA-MOD
mean_100ep_reward = -np.inf
else:
mean_100ep_reward = round(
float(np.mean(episode_rewards[-101:-1])), 1) #MA-MOD
# below is what's logged in terminal.
num_episodes = len(episode_rewards) #MA-MOD
if self.verbose >= 1 and done.any(
) and log_interval is not None and len(
episode_rewards) % log_interval == 0: #MA-MOD
logger.record_tabular("steps", self.num_timesteps)
logger.record_tabular("episodes", num_episodes)
if len(episode_successes) > 0:
logger.logkv("success rate",
np.mean(episode_successes[-100:]))
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward)
logger.record_tabular(
"% time spent exploring",
int(100 * self.exploration.value(self.num_timesteps)))
logger.dump_tabular()
return self
def predict(
self,
observation,
agent_idx,
state=None,
mask=None,
deterministic=True): # MA-MOD - added `agent_idx` as a parameter
observation = np.array(observation)
vectorized_env = self._is_vectorized_observation(
observation, self.observation_space)
observation = observation.reshape((-1, ) +
self.observation_space.shape)
with self.sess.as_default():
actions, _, _ = self.step_model[agent_idx].step(
observation, deterministic=deterministic)
if not vectorized_env:
actions = actions[0]
return actions, None
# No one ever calls this, so we don't need it?
def action_probability(self,
observation,
state=None,
mask=None,
actions=None,
logp=False):
print("Should not be called")
return None
'''
observation = np.array(observation)
vectorized_env = self._is_vectorized_observation(observation, self.observation_space)
observation = observation.reshape((-1,) + self.observation_space.shape)
actions_proba = self.proba_step(observation, state, mask)
if actions is not None: # comparing the action distribution, to given actions
actions = np.array([actions])
assert isinstance(self.action_space, gym.spaces.Discrete)
actions = actions.reshape((-1,))
assert observation.shape[0] == actions.shape[0], "Error: batch sizes differ for actions and observations."
actions_proba = actions_proba[np.arange(actions.shape[0]), actions]
# normalize action proba shape
actions_proba = actions_proba.reshape((-1, 1))
if logp:
actions_proba = np.log(actions_proba)
if not vectorized_env:
if state is not None:
raise ValueError("Error: The environment must be vectorized when using recurrent policies.")
actions_proba = actions_proba[0]
return actions_proba
'''
def get_parameter_list(self):
print(self.params)
return self.params
def save(self, save_path, cloudpickle=False):
# params
data = {
"double_q": self.double_q,
"param_noise": self.param_noise,
"learning_starts": self.learning_starts,
"train_freq": self.train_freq,
"prioritized_replay": self.prioritized_replay,
"prioritized_replay_eps": self.prioritized_replay_eps,
"batch_size": self.batch_size,
"target_network_update_freq": self.target_network_update_freq,
"prioritized_replay_alpha": self.prioritized_replay_alpha,
"prioritized_replay_beta0": self.prioritized_replay_beta0,
"prioritized_replay_beta_iters":
self.prioritized_replay_beta_iters,
"exploration_final_eps": self.exploration_final_eps,
"exploration_fraction": self.exploration_fraction,
"learning_rate": self.learning_rate,
"gamma": self.gamma,
"verbose": self.verbose,
"observation_space": self.observation_space,
"action_space": self.action_space,
"policy": self.policy,
"n_envs": self.n_envs,
"n_cpu_tf_sess": self.n_cpu_tf_sess,
"seed": self.seed,
"_vectorize_action": self._vectorize_action,
"policy_kwargs": self.policy_kwargs,
"num_agents": self.num_agents
}
params_to_save = self.get_parameters()
# print(params_to_save)
self._save_to_file(save_path,
data=data,
params=params_to_save,
cloudpickle=cloudpickle)
class TD3(OffPolicyRLModel):
"""
Twin Delayed DDPG (TD3)
Addressing Function Approximation Error in Actor-Critic Methods.
Original implementation: https://github.com/sfujim/TD3
Paper: https://arxiv.org/pdf/1802.09477.pdf
Introduction to TD3: https://spinningup.openai.com/en/latest/algorithms/td3.html
:param policy: (TD3Policy or str) The policy model to use (MlpPolicy, CnnPolicy, LnMlpPolicy, ...)
:param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
:param gamma: (float) the discount factor
:param learning_rate: (float or callable) learning rate for adam optimizer,
the same learning rate will be used for all networks (Q-Values and Actor networks)
it can be a function of the current progress (from 1 to 0)
:param buffer_size: (int) size of the replay buffer
:param batch_size: (int) Minibatch size for each gradient update
:param tau: (float) the soft update coefficient ("polyak update" of the target networks, between 0 and 1)
:param policy_delay: (int) Policy and target networks will only be updated once every policy_delay steps
per training steps. The Q values will be updated policy_delay more often (update every training step).
:param action_noise: (ActionNoise) the action noise type. Cf DDPG for the different action noise type.
:param target_policy_noise: (float) Standard deviation of gaussian noise added to target policy
(smoothing noise)
:param target_noise_clip: (float) Limit for absolute value of target policy smoothing noise.
:param train_freq: (int) Update the model every `train_freq` steps.
:param learning_starts: (int) how many steps of the model to collect transitions for before learning starts
:param gradient_steps: (int) How many gradient update after each step
:param random_exploration: (float) Probability of taking a random action (as in an epsilon-greedy strategy)
This is not needed for TD3 normally but can help exploring when using HER + TD3.
This hack was present in the original OpenAI Baselines repo (DDPG + HER)
:param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
:param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
:param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
:param policy_kwargs: (dict) additional arguments to be passed to the policy on creation
:param full_tensorboard_log: (bool) enable additional logging when using tensorboard
Note: this has no effect on TD3 logging for now
:param seed: (int) Seed for the pseudo-random generators (python, numpy, tensorflow).
If None (default), use random seed. Note that if you want completely deterministic
results, you must set `n_cpu_tf_sess` to 1.
:param n_cpu_tf_sess: (int) The number of threads for TensorFlow operations
If None, the number of cpu of the current machine will be used.
"""
def __init__(self,
policy,
env,
gamma=0.99,
learning_rate=3e-4,
buffer_size=50000,
learning_starts=100,
train_freq=100,
gradient_steps=100,
batch_size=128,
tau=0.005,
policy_delay=2,
action_noise=None,
target_policy_noise=0.2,
target_noise_clip=0.5,
random_exploration=0.0,
verbose=0,
tensorboard_log=None,
_init_setup_model=True,
policy_kwargs=None,
full_tensorboard_log=False,
seed=None,
n_cpu_tf_sess=None):
super(TD3, self).__init__(policy=policy,
env=env,
replay_buffer=None,
verbose=verbose,
policy_base=TD3Policy,
requires_vec_env=False,
policy_kwargs=policy_kwargs,
seed=seed,
n_cpu_tf_sess=n_cpu_tf_sess)
self.buffer_size = buffer_size
self.learning_rate = learning_rate
self.learning_starts = learning_starts
self.train_freq = train_freq
self.batch_size = batch_size
self.tau = tau
self.gradient_steps = gradient_steps
self.gamma = gamma
self.action_noise = action_noise
self.random_exploration = random_exploration
self.policy_delay = policy_delay
self.target_noise_clip = target_noise_clip
self.target_policy_noise = target_policy_noise
self.graph = None
self.replay_buffer = None
self.episode_reward = None
self.sess = None
self.tensorboard_log = tensorboard_log
self.verbose = verbose
self.params = None
self.summary = None
self.policy_tf = None
self.full_tensorboard_log = full_tensorboard_log
self.obs_target = None
self.target_policy_tf = None
self.actions_ph = None
self.rewards_ph = None
self.terminals_ph = None
self.observations_ph = None
self.action_target = None
self.next_observations_ph = None
self.step_ops = None
self.target_ops = None
self.infos_names = None
self.target_params = None
self.learning_rate_ph = None
self.processed_obs_ph = None
self.processed_next_obs_ph = None
self.policy_out = None
self.policy_train_op = None
self.policy_loss = None
if _init_setup_model:
self.setup_model()
def _get_pretrain_placeholders(self):
policy = self.policy_tf
# Rescale
policy_out = unscale_action(self.action_space, self.policy_out)
return policy.obs_ph, self.actions_ph, policy_out
def setup_model(self):
with SetVerbosity(self.verbose):
self.graph = tf.Graph()
with self.graph.as_default():
self.set_random_seed(self.seed)
self.sess = tf_util.make_session(num_cpu=self.n_cpu_tf_sess,
graph=self.graph)
self.replay_buffer = ReplayBuffer(self.buffer_size)
with tf.variable_scope("input", reuse=False):
# Create policy and target TF objects
self.policy_tf = self.policy(self.sess,
self.observation_space,
self.action_space,
**self.policy_kwargs)
self.target_policy_tf = self.policy(
self.sess, self.observation_space, self.action_space,
**self.policy_kwargs)
# Initialize Placeholders
self.observations_ph = self.policy_tf.obs_ph
# Normalized observation for pixels
self.processed_obs_ph = self.policy_tf.processed_obs
self.next_observations_ph = self.target_policy_tf.obs_ph
self.processed_next_obs_ph = self.target_policy_tf.processed_obs
self.action_target = self.target_policy_tf.action_ph
self.terminals_ph = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals')
self.rewards_ph = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.actions_ph = tf.placeholder(tf.float32,
shape=(None, ) +
self.action_space.shape,
name='actions')
self.learning_rate_ph = tf.placeholder(
tf.float32, [], name="learning_rate_ph")
with tf.variable_scope("model", reuse=False):
# Create the policy
self.policy_out = policy_out = self.policy_tf.make_actor(
self.processed_obs_ph)
# Use two Q-functions to improve performance by reducing overestimation bias
qf1, qf2 = self.policy_tf.make_critics(
self.processed_obs_ph, self.actions_ph)
# Q value when following the current policy
qf1_pi, _ = self.policy_tf.make_critics(
self.processed_obs_ph, policy_out, reuse=True)
with tf.variable_scope("target", reuse=False):
# Create target networks
target_policy_out = self.target_policy_tf.make_actor(
self.processed_next_obs_ph)
# Target policy smoothing, by adding clipped noise to target actions
target_noise = tf.random_normal(
tf.shape(target_policy_out),
stddev=self.target_policy_noise)
target_noise = tf.clip_by_value(target_noise,
-self.target_noise_clip,
self.target_noise_clip)
# Clip the noisy action to remain in the bounds [-1, 1] (output of a tanh)
noisy_target_action = tf.clip_by_value(
target_policy_out + target_noise, -1, 1)
# Q values when following the target policy
qf1_target, qf2_target = self.target_policy_tf.make_critics(
self.processed_next_obs_ph, noisy_target_action)
with tf.variable_scope("loss", reuse=False):
# Take the min of the two target Q-Values (clipped Double-Q Learning)
min_qf_target = tf.minimum(qf1_target, qf2_target)
# Targets for Q value regression
q_backup = tf.stop_gradient(self.rewards_ph +
(1 - self.terminals_ph) *
self.gamma * min_qf_target)
# Compute Q-Function loss
qf1_loss = tf.reduce_mean((q_backup - qf1)**2)
qf2_loss = tf.reduce_mean((q_backup - qf2)**2)
qvalues_losses = qf1_loss + qf2_loss
# Policy loss: maximise q value
self.policy_loss = policy_loss = -tf.reduce_mean(qf1_pi)
# Policy train op
# will be called only every n training steps,
# where n is the policy delay
policy_optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate_ph)
policy_train_op = policy_optimizer.minimize(
policy_loss, var_list=get_vars('model/pi'))
self.policy_train_op = policy_train_op
# Q Values optimizer
qvalues_optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate_ph)
qvalues_params = get_vars('model/values_fn/')
# Q Values and policy target params
source_params = get_vars("model/")
target_params = get_vars("target/")
# Polyak averaging for target variables
self.target_ops = [
tf.assign(target,
(1 - self.tau) * target + self.tau * source)
for target, source in zip(target_params, source_params)
]
# Initializing target to match source variables
target_init_op = [
tf.assign(target, source)
for target, source in zip(target_params, source_params)
]
train_values_op = qvalues_optimizer.minimize(
qvalues_losses, var_list=qvalues_params)
self.infos_names = ['qf1_loss', 'qf2_loss']
# All ops to call during one training step
self.step_ops = [
qf1_loss, qf2_loss, qf1, qf2, train_values_op
]
# Monitor losses and entropy in tensorboard
tf.summary.scalar('policy_loss', policy_loss)
tf.summary.scalar('qf1_loss', qf1_loss)
tf.summary.scalar('qf2_loss', qf2_loss)
tf.summary.scalar('learning_rate',
tf.reduce_mean(self.learning_rate_ph))
# Retrieve parameters that must be saved
self.params = get_vars("model")
self.target_params = get_vars("target/")
# Initialize Variables and target network
with self.sess.as_default():
self.sess.run(tf.global_variables_initializer())
self.sess.run(target_init_op)
self.summary = tf.summary.merge_all()
def _train_step(self, step, writer, learning_rate, update_policy):
# Sample a batch from the replay buffer
batch = self.replay_buffer.sample(self.batch_size)
batch_obs, batch_actions, batch_rewards, batch_next_obs, batch_dones = batch
feed_dict = {
self.observations_ph: batch_obs,
self.actions_ph: batch_actions,
self.next_observations_ph: batch_next_obs,
self.rewards_ph: batch_rewards.reshape(self.batch_size, -1),
self.terminals_ph: batch_dones.reshape(self.batch_size, -1),
self.learning_rate_ph: learning_rate
}
step_ops = self.step_ops
if update_policy:
# Update policy and target networks
step_ops = step_ops + [
self.policy_train_op, self.target_ops, self.policy_loss
]
# Do one gradient step
# and optionally compute log for tensorboard
if writer is not None:
out = self.sess.run([self.summary] + step_ops, feed_dict)
summary = out.pop(0)
writer.add_summary(summary, step)
else:
out = self.sess.run(step_ops, feed_dict)
# Unpack to monitor losses
qf1_loss, qf2_loss, *_values = out
return qf1_loss, qf2_loss
def learn(self,
total_timesteps,
callback=None,
log_interval=4,
tb_log_name="TD3",
reset_num_timesteps=True,
replay_wrapper=None):
new_tb_log = self._init_num_timesteps(reset_num_timesteps)
if replay_wrapper is not None:
self.replay_buffer = replay_wrapper(self.replay_buffer)
with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \
as writer:
self._setup_learn()
# Transform to callable if needed
self.learning_rate = get_schedule_fn(self.learning_rate)
# Initial learning rate
current_lr = self.learning_rate(1)
start_time = time.time()
episode_rewards = [0.0]
episode_successes = []
if self.action_noise is not None:
self.action_noise.reset()
obs = self.env.reset()
self.episode_reward = np.zeros((1, ))
ep_info_buf = deque(maxlen=100)
n_updates = 0
infos_values = []
for step in range(total_timesteps):
if callback is not None:
# Only stop training if return value is False, not when it is None. This is for backwards
# compatibility with callbacks that have no return statement.
if callback(locals(), globals()) is False:
break
# Before training starts, randomly sample actions
# from a uniform distribution for better exploration.
# Afterwards, use the learned policy
# if random_exploration is set to 0 (normal setting)
if self.num_timesteps < self.learning_starts or np.random.rand(
) < self.random_exploration:
# actions sampled from action space are from range specific to the environment
# but algorithm operates on tanh-squashed actions therefore simple scaling is used
unscaled_action = self.env.action_space.sample()
action = scale_action(self.action_space, unscaled_action)
else:
action = self.policy_tf.step(obs[None]).flatten()
# Add noise to the action, as the policy
# is deterministic, this is required for exploration
if self.action_noise is not None:
action = np.clip(action + self.action_noise(), -1, 1)
# Rescale from [-1, 1] to the correct bounds
unscaled_action = unscale_action(self.action_space, action)
assert action.shape == self.env.action_space.shape
new_obs, reward, done, info = self.env.step(unscaled_action)
# Store transition in the replay buffer.
self.replay_buffer.add(obs, action, reward, new_obs,
float(done))
obs = new_obs
# Retrieve reward and episode length if using Monitor wrapper
maybe_ep_info = info.get('episode')
if maybe_ep_info is not None:
ep_info_buf.extend([maybe_ep_info])
if writer is not None:
# Write reward per episode to tensorboard
ep_reward = np.array([reward]).reshape((1, -1))
ep_done = np.array([done]).reshape((1, -1))
self.episode_reward = total_episode_reward_logger(
self.episode_reward, ep_reward, ep_done, writer,
self.num_timesteps)
if step % self.train_freq == 0:
mb_infos_vals = []
# Update policy, critics and target networks
for grad_step in range(self.gradient_steps):
# Break if the warmup phase is not over
# or if there are not enough samples in the replay buffer
if not self.replay_buffer.can_sample(self.batch_size) \
or self.num_timesteps < self.learning_starts:
break
n_updates += 1
# Compute current learning_rate
frac = 1.0 - step / total_timesteps
current_lr = self.learning_rate(frac)
# Update policy and critics (q functions)
# Note: the policy is updated less frequently than the Q functions
# this is controlled by the `policy_delay` parameter
mb_infos_vals.append(
self._train_step(step, writer, current_lr,
(step + grad_step) %
self.policy_delay == 0))
# Log losses and entropy, useful for monitor training
if len(mb_infos_vals) > 0:
infos_values = np.mean(mb_infos_vals, axis=0)
episode_rewards[-1] += reward
if done:
if self.action_noise is not None:
self.action_noise.reset()
if not isinstance(self.env, VecEnv):
obs = self.env.reset()
episode_rewards.append(0.0)
maybe_is_success = info.get('is_success')
if maybe_is_success is not None:
episode_successes.append(float(maybe_is_success))
if len(episode_rewards[-101:-1]) == 0:
mean_reward = -np.inf
else:
mean_reward = round(
float(np.mean(episode_rewards[-101:-1])), 1)
num_episodes = len(episode_rewards)
self.num_timesteps += 1
# Display training infos
if self.verbose >= 1 and done and log_interval is not None and len(
episode_rewards) % log_interval == 0:
fps = int(step / (time.time() - start_time))
logger.logkv("episodes", num_episodes)
logger.logkv("mean 100 episode reward", mean_reward)
if len(ep_info_buf) > 0 and len(ep_info_buf[0]) > 0:
logger.logkv(
'ep_rewmean',
safe_mean(
[ep_info['r'] for ep_info in ep_info_buf]))
logger.logkv(
'eplenmean',
safe_mean(
[ep_info['l'] for ep_info in ep_info_buf]))
logger.logkv("n_updates", n_updates)
logger.logkv("current_lr", current_lr)
logger.logkv("fps", fps)
logger.logkv('time_elapsed', int(time.time() - start_time))
if len(episode_successes) > 0:
logger.logkv("success rate",
np.mean(episode_successes[-100:]))
if len(infos_values) > 0:
for (name, val) in zip(self.infos_names, infos_values):
logger.logkv(name, val)
logger.logkv("total timesteps", self.num_timesteps)
logger.dumpkvs()
# Reset infos:
infos_values = []
return self
def action_probability(self,
observation,
state=None,
mask=None,
actions=None,
logp=False):
_ = np.array(observation)
if actions is not None:
raise ValueError("Error: TD3 does not have action probabilities.")
# here there are no action probabilities, as DDPG does not use a probability distribution
warnings.warn(
"Warning: action probability is meaningless for TD3. Returning None"
)
return None
def predict(self, observation, state=None, mask=None, deterministic=True):
observation = np.array(observation)
vectorized_env = self._is_vectorized_observation(
observation, self.observation_space)
observation = observation.reshape((-1, ) +
self.observation_space.shape)
actions = self.policy_tf.step(observation)
if self.action_noise is not None and not deterministic:
actions = np.clip(actions + self.action_noise(), -1, 1)
actions = actions.reshape(
(-1, ) +
self.action_space.shape) # reshape to the correct action shape
actions = unscale_action(
self.action_space, actions) # scale the output for the prediction
if not vectorized_env:
actions = actions[0]
return actions, None
def get_parameter_list(self):
return (self.params + self.target_params)
def save(self, save_path, cloudpickle=False):
data = {
"learning_rate": self.learning_rate,
"buffer_size": self.buffer_size,
"learning_starts": self.learning_starts,
"train_freq": self.train_freq,
"batch_size": self.batch_size,
"tau": self.tau,
# Should we also store the replay buffer?
# this may lead to high memory usage
# with all transition inside
# "replay_buffer": self.replay_buffer
"policy_delay": self.policy_delay,
"target_noise_clip": self.target_noise_clip,
"target_policy_noise": self.target_policy_noise,
"gamma": self.gamma,
"verbose": self.verbose,
"observation_space": self.observation_space,
"action_space": self.action_space,
"policy": self.policy,
"n_envs": self.n_envs,
"n_cpu_tf_sess": self.n_cpu_tf_sess,
"seed": self.seed,
"action_noise": self.action_noise,
"random_exploration": self.random_exploration,
"_vectorize_action": self._vectorize_action,
"policy_kwargs": self.policy_kwargs
}
params_to_save = self.get_parameters()
self._save_to_file(save_path,
data=data,
params=params_to_save,
cloudpickle=cloudpickle)
class MyDQN(DQN):
def __init__(self, *args, **kwargs):
super(MyDQN, self).__init__(*args, **kwargs)
def setup_model(self):
with SetVerbosity(self.verbose):
assert not isinstance(self.action_space, gym.spaces.Box), \
"Error: DQN cannot output a gym.spaces.Box action space."
# If the policy is wrap in functool.partial (e.g. to disable dueling)
# unwrap it to check the class type
if isinstance(self.policy, partial):
test_policy = self.policy.func
else:
test_policy = self.policy
assert issubclass(test_policy, DQNPolicy), "Error: the input policy for the DQN model must be " \
"an instance of DQNPolicy."
self.graph = tf.Graph()
with self.graph.as_default():
self.sess = tf_util.make_session(graph=self.graph)
optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate)
self.act, self._train_step, self.update_target, self.step_model = my_build_train(
q_func=partial(self.policy, **self.policy_kwargs),
ob_space=self.observation_space,
ac_space=self.action_space,
optimizer=optimizer,
gamma=self.gamma,
grad_norm_clipping=10,
param_noise=self.param_noise,
sess=self.sess,
full_tensorboard_log=self.full_tensorboard_log,
double_q=self.double_q)
self.proba_step = self.step_model.proba_step
self.params = tf_util.get_trainable_vars("deepq")
# Initialize the parameters and copy them to the target network.
tf_util.initialize(self.sess)
self.update_target(sess=self.sess)
self.summary = tf.summary.merge_all()
def learn(self,
total_timesteps,
callback=None,
seed=None,
log_interval=100,
tb_log_name="DQN",
reset_num_timesteps=True,
replay_wrapper=None):
new_tb_log = self._init_num_timesteps(reset_num_timesteps)
with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \
as writer:
self._setup_learn(seed)
# Create the replay buffer
if self.prioritized_replay:
self.replay_buffer = PrioritizedReplayBuffer(
self.buffer_size, alpha=self.prioritized_replay_alpha)
if self.prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
else:
prioritized_replay_beta_iters = self.prioritized_replay_beta_iters
self.beta_schedule = LinearSchedule(
prioritized_replay_beta_iters,
initial_p=self.prioritized_replay_beta0,
final_p=1.0)
else:
self.replay_buffer = ReplayBuffer(self.buffer_size)
self.beta_schedule = None
if replay_wrapper is not None:
assert not self.prioritized_replay, "Prioritized replay buffer is not supported by HER"
self.replay_buffer = replay_wrapper(self.replay_buffer)
# Create the schedule for exploration starting from 1.
self.exploration = LinearSchedule(
schedule_timesteps=int(self.exploration_fraction *
total_timesteps),
initial_p=1.0,
final_p=self.exploration_final_eps)
episode_rewards = [0.0]
episode_successes = []
Globals.env = self.env
obs = self.env.reset()
reset = True
self.episode_reward = np.zeros((1, ))
timesteps_last_log = 0
avr_ep_len_per_log = None
sleep = 0.045
for _ in range(total_timesteps):
if Globals.loading:
Globals.loading = False
while Globals.pause_game:
pass
if Globals.exit_learning:
break
if callback is not None:
# Only stop training if return value is False, not when it is None. This is for backwards
# compatibility with callbacks that have no return statement.
if callback(locals(), globals()) is False:
break
# Take action and update exploration to the newest value
kwargs = {}
if not self.param_noise:
update_eps = self.exploration.value(self.num_timesteps)
update_param_noise_threshold = 0.
else:
update_eps = 0.
# Compute the threshold such that the KL divergence between perturbed and non-perturbed
# policy is comparable to eps-greedy exploration with eps = exploration.value(t).
# See Appendix C.1 in Parameter Space Noise for Exploration, Plappert et al., 2017
# for detailed explanation.
update_param_noise_threshold = \
-np.log(1. - self.exploration.value(self.num_timesteps) +
self.exploration.value(self.num_timesteps) / float(self.env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
with self.sess.as_default():
action = self.act(np.array(obs)[None],
update_eps=update_eps,
**kwargs)[0]
env_action = action
reset = False
new_obs, rew, done, info = self.env.step(env_action)
# Store transition in the replay buffer.
self.replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
if writer is not None:
ep_rew = np.array([rew]).reshape((1, -1))
ep_done = np.array([done]).reshape((1, -1))
self.episode_reward = total_episode_reward_logger(
self.episode_reward, ep_rew, ep_done, writer,
self.num_timesteps)
episode_rewards[-1] += rew
if done:
maybe_is_success = info.get('is_success')
if maybe_is_success is not None:
episode_successes.append(float(maybe_is_success))
if not isinstance(self.env, VecEnv):
obs = self.env.reset()
episode_rewards.append(0.0)
reset = True
# Do not train if the warmup phase is not over
# or if there are not enough samples in the replay buffer
can_sample = self.replay_buffer.can_sample(self.batch_size)
if can_sample:
sleep = 0.035
time.sleep(sleep)
if can_sample and self.num_timesteps > self.learning_starts \
and self.num_timesteps % self.train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if self.prioritized_replay:
experience = self.replay_buffer.sample(
self.batch_size,
beta=self.beta_schedule.value(self.num_timesteps))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = self.replay_buffer.sample(
self.batch_size)
weights, batch_idxes = np.ones_like(rewards), None
if writer is not None:
# run loss backprop with summary, but once every 100 steps save the metadata
# (memory, compute time, ...)
if (1 + self.num_timesteps) % 100 == 0:
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary, td_errors = self._train_step(
obses_t,
actions,
rewards,
obses_tp1,
obses_tp1,
dones,
weights,
sess=self.sess,
options=run_options,
run_metadata=run_metadata)
writer.add_run_metadata(
run_metadata, 'step%d' % self.num_timesteps)
else:
summary, td_errors = self._train_step(
obses_t,
actions,
rewards,
obses_tp1,
obses_tp1,
dones,
weights,
sess=self.sess)
writer.add_summary(summary, self.num_timesteps)
else:
_, td_errors = self._train_step(obses_t,
actions,
rewards,
obses_tp1,
obses_tp1,
dones,
weights,
sess=self.sess)
if self.prioritized_replay:
new_priorities = np.abs(
td_errors) + self.prioritized_replay_eps
self.replay_buffer.update_priorities(
batch_idxes, new_priorities)
if can_sample and self.num_timesteps > self.learning_starts and \
self.num_timesteps % self.target_network_update_freq == 0:
# Update target network periodically.
self.update_target(sess=self.sess)
if len(episode_rewards[-101:-1]) == 0:
mean_100ep_reward = -np.inf
else:
mean_100ep_reward = round(
float(np.mean(episode_rewards[-101:-1])), 1)
if len(episode_rewards) % log_interval == 0:
avr_ep_len_per_log = (self.num_timesteps -
timesteps_last_log) / log_interval
timesteps_last_log = self.num_timesteps
num_episodes = len(episode_rewards)
if self.verbose >= 1 and done and log_interval is not None and len(
episode_rewards) % log_interval == 0:
logger.record_tabular("steps", self.num_timesteps)
logger.record_tabular("episodes", num_episodes)
if len(episode_successes) > 0:
logger.logkv("success rate",
np.mean(episode_successes[-100:]))
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward)
logger.record_tabular(
"% time spent exploring",
int(100 * self.exploration.value(self.num_timesteps)))
logger.record_tabular("avr length of last logged ep",
avr_ep_len_per_log)
logger.dump_tabular()
self.num_timesteps += 1
Globals.steps -= 1
return self
def evaluate(self, n_episodes=2):
logging.basicConfig(level=logging.INFO)
id = 'BreakoutNoFrameskip-v4'
num_env = 1
n_stack = 4
left_lives = 5
seed = 0
episodes = 0
score = 0
frames = 0
frames_per_episode = list()
scores = [list() for i in range(n_episodes)]
env = make_atari_env(id, num_env=num_env, seed=seed)
env = VecFrameStack(env, n_stack=n_stack)
obs = env.reset()
while (n_episodes - episodes) > 0:
frames += 1
action, _states = self.predict(obs)
obs, rewards, dones, info = env.step(action)
env.render()
score += rewards[0]
if dones:
logging.debug('You died')
logging.debug(f'Score = {score}')
scores[episodes].append(score)
score = 0
left_lives -= 1
if not left_lives:
logging.debug('Episode ended')
logging.info(f'Scores per life: {scores[episodes]}')
frames_per_episode.append(frames)
frames = 0
episodes += 1
left_lives = 5
s = list(map(sum, scores))
avg_s = int(sum(s) / len(s))
avg_f = int(sum(frames_per_episode) / len(frames_per_episode))
logging.info(f'Played {n_episodes} episodes')
logging.info(f'Scores per episode : {s}')
logging.info(f'Average score per episode : {avg_s}')
logging.info(f'Average number of frames per episode : {avg_f}')
return avg_f, avg_s
class DQN(OffPolicyRLModel):
"""
The DQN model class.
DQN paper: https://arxiv.org/abs/1312.5602
Dueling DQN: https://arxiv.org/abs/1511.06581
Double-Q Learning: https://arxiv.org/abs/1509.06461
Prioritized Experience Replay: https://arxiv.org/abs/1511.05952
:param policy: (DQNPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, LnMlpPolicy, ...)
:param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
:param gamma: (float) discount factor
:param learning_rate: (float) learning rate for adam optimizer
:param buffer_size: (int) size of the replay buffer
:param exploration_fraction: (float) fraction of entire training period over which the exploration rate is
annealed
:param exploration_final_eps: (float) final value of random action probability
:param exploration_initial_eps: (float) initial value of random action probability
:param train_freq: (int) update the model every `train_freq` steps. set to None to disable printing
:param batch_size: (int) size of a batched sampled from replay buffer for training
:param double_q: (bool) Whether to enable Double-Q learning or not.
:param learning_starts: (int) how many steps of the model to collect transitions for before learning starts
:param target_network_update_freq: (int) update the target network every `target_network_update_freq` steps.
:param prioritized_replay: (bool) if True prioritized replay buffer will be used.
:param prioritized_replay_alpha: (float)alpha parameter for prioritized replay buffer.
It determines how much prioritization is used, with alpha=0 corresponding to the uniform case.
:param prioritized_replay_beta0: (float) initial value of beta for prioritized replay buffer
:param prioritized_replay_beta_iters: (int) number of iterations over which beta will be annealed from initial
value to 1.0. If set to None equals to max_timesteps.
:param prioritized_replay_eps: (float) epsilon to add to the TD errors when updating priorities.
:param param_noise: (bool) Whether or not to apply noise to the parameters of the policy.
:param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
:param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
:param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
:param full_tensorboard_log: (bool) enable additional logging when using tensorboard
WARNING: this logging can take a lot of space quickly
:param seed: (int) Seed for the pseudo-random generators (python, numpy, tensorflow).
If None (default), use random seed. Note that if you want completely deterministic
results, you must set `n_cpu_tf_sess` to 1.
:param n_cpu_tf_sess: (int) The number of threads for TensorFlow operations
If None, the number of cpu of the current machine will be used.
"""
def _create_replay_wrapper(self, env):
"""
Wrap the environment in a HERGoalEnvWrapper
if needed and create the replay buffer wrapper.
"""
if not isinstance(env, HERGoalEnvWrapper):
env = HERGoalEnvWrapper(env)
self.env = env
self.n_sampled_goal = 4
self.goal_selection_strategy = 'future'
# NOTE: we cannot do that check directly with VecEnv
# maybe we can try calling `compute_reward()` ?
# assert isinstance(self.env, gym.GoalEnv), "HER only supports gym.GoalEnv"
self.replay_wrapper = functools.partial(
HindsightExperienceReplayWrapper,
n_sampled_goal=self.n_sampled_goal,
goal_selection_strategy=self.goal_selection_strategy,
wrapped_env=self.env)
def __init__(self,
policy,
env,
gamma=0.99,
learning_rate=5e-4,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
exploration_initial_eps=1.0,
train_freq=1,
batch_size=32,
double_q=True,
learning_starts=1000,
target_network_update_freq=500,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
n_cpu_tf_sess=None,
verbose=0,
tensorboard_log=None,
_init_setup_model=True,
policy_kwargs=None,
full_tensorboard_log=False,
seed=None,
n_actions=2):
# TODO: replay_buffer refactoring
super(DQN, self).__init__(policy=policy,
env=env,
replay_buffer=None,
verbose=verbose,
policy_base=DQNPolicy,
requires_vec_env=False,
policy_kwargs=policy_kwargs,
seed=seed,
n_cpu_tf_sess=n_cpu_tf_sess)
# for HER algorithm
if self.env is not None and 'Fetch' in self.env.__str__():
self._create_replay_wrapper(self.env)
self.observation_space = self.env.observation_space
self.param_noise = param_noise
self.learning_starts = learning_starts
self.train_freq = train_freq
self.prioritized_replay = prioritized_replay
self.prioritized_replay_eps = prioritized_replay_eps
self.batch_size = batch_size
self.target_network_update_freq = target_network_update_freq
self.prioritized_replay_alpha = prioritized_replay_alpha
self.prioritized_replay_beta0 = prioritized_replay_beta0
self.prioritized_replay_beta_iters = prioritized_replay_beta_iters
self.exploration_final_eps = exploration_final_eps
self.exploration_initial_eps = exploration_initial_eps
self.exploration_fraction = exploration_fraction
self.buffer_size = buffer_size
self.learning_rate = learning_rate
self.gamma = gamma
self.tensorboard_log = tensorboard_log
self.full_tensorboard_log = full_tensorboard_log
self.double_q = double_q
self.graph = None
self.sess = None
self._train_step = None
self.step_model = None
self.update_target = None
self.act = None
self.proba_step = None
self.replay_buffer = None
self.beta_schedule = None
self.exploration = None
self.params = None
self.summary = None
self.n_actions = n_actions
self.macro_len = 5
self.macro_count = 0 # when macro_count % macro_len == 0, resample macro action
if _init_setup_model:
self.setup_model()
def _get_pretrain_placeholders(self):
policy = self.step_model
return policy.obs_ph, tf.placeholder(tf.int32, [None]), policy.q_values
def setup_model(self):
with SetVerbosity(self.verbose):
# decision net: produce categorical distribution
self.action_space = gym.spaces.discrete.Discrete(self.n_actions)
assert not isinstance(self.action_space, gym.spaces.Box), \
"Error: DQN cannot output a gym.spaces.Box action space."
# If the policy is wrap in functool.partial (e.g. to disable dueling)
# unwrap it to check the class type
if isinstance(self.policy, partial):
test_policy = self.policy.func
else:
test_policy = self.policy
assert issubclass(test_policy, DQNPolicy), "Error: the input policy for the DQN model must be " \
"an instance of DQNPolicy."
self.graph = tf.Graph()
with self.graph.as_default():
self.set_random_seed(self.seed)
self.sess = tf_util.make_session(num_cpu=self.n_cpu_tf_sess,
graph=self.graph)
optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate)
self.act, self._train_step, self.update_target, self.step_model = build_train(
q_func=partial(self.policy, **self.policy_kwargs),
ob_space=self.observation_space,
ac_space=self.action_space,
optimizer=optimizer,
gamma=self.gamma,
grad_norm_clipping=10,
param_noise=self.param_noise,
sess=self.sess,
full_tensorboard_log=self.full_tensorboard_log,
double_q=self.double_q)
self.proba_step = self.step_model.proba_step
self.params = tf_util.get_trainable_vars("deepq")
# Initialize the parameters and copy them to the target network.
tf_util.initialize(self.sess)
self.update_target(sess=self.sess)
self.summary = tf.summary.merge_all()
def learn(self,
total_timesteps,
callback=None,
log_interval=100,
tb_log_name="DQN",
reset_num_timesteps=True,
replay_wrapper=None,
distinct_replay_buffer=False):
new_tb_log = self._init_num_timesteps(reset_num_timesteps)
for i, m in enumerate(self.sub_models):
m.learning_rate = get_schedule_fn(m.learning_rate)
if len(self.replay_wrappers) != 0:
m.replay_buffer = self.replay_wrappers[i](m.replay_buffer)
m._setup_learn()
with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \
as writer:
self._setup_learn()
# Create the replay buffer
if self.prioritized_replay:
self.replay_buffer = PrioritizedReplayBuffer(
self.buffer_size, alpha=self.prioritized_replay_alpha)
if self.prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
else:
prioritized_replay_beta_iters = self.prioritized_replay_beta_iters
self.beta_schedule = LinearSchedule(
prioritized_replay_beta_iters,
initial_p=self.prioritized_replay_beta0,
final_p=1.0)
else:
self.replay_buffer = ReplayBuffer(self.buffer_size)
self.beta_schedule = None
if replay_wrapper is not None:
assert not self.prioritized_replay, "Prioritized replay buffer is not supported by HER"
self.replay_buffer = replay_wrapper(self.replay_buffer)
# Create the schedule for exploration starting from 1.
self.exploration = LinearSchedule(
schedule_timesteps=int(self.exploration_fraction *
total_timesteps),
initial_p=self.exploration_initial_eps,
final_p=self.exploration_final_eps)
episode_rewards = [0.0]
episode_successes = []
obs = self.env.reset()
reset = True
macro_count = 0
macro_len = self.macro_len
macro_choices = []
n_updates = 0
for step in range(total_timesteps):
if callback is not None:
# Only stop training if return value is False, not when it is None. This is for backwards
# compatibility with callbacks that have no return statement.
if callback(locals(), globals()) is False:
break
# Take action and update exploration to the newest value
kwargs = {}
if not self.param_noise:
update_eps = self.exploration.value(self.num_timesteps)
update_param_noise_threshold = 0.
else:
update_eps = 0.
# Compute the threshold such that the KL divergence between perturbed and non-perturbed
# policy is comparable to eps-greedy exploration with eps = exploration.value(t).
# See Appendix C.1 in Parameter Space Noise for Exploration, Plappert et al., 2017
# for detailed explanation.
update_param_noise_threshold = \
-np.log(1. - self.exploration.value(self.num_timesteps) +
self.exploration.value(self.num_timesteps) / float(self.env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
with self.sess.as_default():
if reset or macro_count % macro_len == 0:
macro_action = self.act(np.array(obs)[None],
update_eps=update_eps,
**kwargs)[0]
# macro_action = 1
macro_obs = obs
reward_in_one_macro = 0
macro_count += 1
macro_choices.append(macro_action)
# use sub_model to decide action
# env_action = self.sub_models[macro_action]
current_sub = self.sub_models[macro_action]
if self.num_timesteps < self.learning_starts or np.random.rand(
) < current_sub.random_exploration:
# actions sampled from action space are from range specific to the environment
# but algorithm operates on tanh-squashed actions therefore simple scaling is used
unscaled_action = self.env.action_space.sample()
action = scale_action(self.env.action_space,
unscaled_action)
else:
action = current_sub.policy_tf.step(
obs[None], deterministic=False).flatten()
# Add noise to the action (improve exploration,
# not needed in general)
if current_sub.action_noise is not None:
action = np.clip(action + current_sub.action_noise(),
-1, 1)
# inferred actions need to be transformed to environment action_space before stepping
unscaled_action = unscale_action(self.env.action_space,
action)
assert action.shape == self.env.action_space.shape
reset = False
new_obs, rew, done, info = self.env.step(unscaled_action)
episode_rewards[-1] += rew
# rew -= self.args.policy_cost_coef * self.args.sub_policy_costs[macro_action]
reward_in_one_macro += rew - self.args.policy_cost_coef * self.args.sub_policy_costs[
macro_action]
# Store transition in the replay buffer.
if macro_count % macro_len == 0 or done:
self.replay_buffer.add(macro_obs, macro_action,
reward_in_one_macro, new_obs,
float(done))
for i, m in enumerate(self.sub_models):
if distinct_replay_buffer:
if i == macro_action:
m.replay_buffer.add(obs, action, rew, new_obs,
float(done))
else:
m.replay_buffer.add(obs, action, rew, new_obs,
float(done))
obs = new_obs
if writer is not None:
ep_rew = np.array([rew]).reshape((1, -1))
ep_done = np.array([done]).reshape((1, -1))
total_episode_reward_logger(self.episode_reward, ep_rew,
ep_done, writer,
self.num_timesteps)
# print("step: %d, done: %d" % (self.num_timesteps, done))
if done:
maybe_is_success = info.get('is_success')
if maybe_is_success is not None:
episode_successes.append(float(maybe_is_success))
if not isinstance(self.env, VecEnv):
obs = self.env.reset()
episode_rewards.append(0.0)
reset = True
macro_action = None
macro_count = 0
prev_macro_choices = macro_choices
macro_choices = []
# Do not train if the warmup phase is not over
# or if there are not enough samples in the replay buffer
can_sample = self.replay_buffer.can_sample(self.batch_size)
if can_sample and self.num_timesteps > self.learning_starts \
and self.num_timesteps % self.train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
# pytype:disable=bad-unpacking
if self.prioritized_replay:
assert self.beta_schedule is not None, \
"BUG: should be LinearSchedule when self.prioritized_replay True"
experience = self.replay_buffer.sample(
self.batch_size,
beta=self.beta_schedule.value(self.num_timesteps))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = self.replay_buffer.sample(
self.batch_size)
weights, batch_idxes = np.ones_like(rewards), None
# pytype:enable=bad-unpacking
if writer is not None:
# run loss backprop with summary, but once every 100 steps save the metadata
# (memory, compute time, ...)
if (1 + self.num_timesteps) % 100 == 0:
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary, td_errors = self._train_step(
obses_t,
actions,
rewards,
obses_tp1,
obses_tp1,
dones,
weights,
sess=self.sess,
options=run_options,
run_metadata=run_metadata)
writer.add_run_metadata(
run_metadata, 'step%d' % self.num_timesteps)
else:
summary, td_errors = self._train_step(
obses_t,
actions,
rewards,
obses_tp1,
obses_tp1,
dones,
weights,
sess=self.sess)
writer.add_summary(summary, self.num_timesteps)
else:
_, td_errors = self._train_step(obses_t,
actions,
rewards,
obses_tp1,
obses_tp1,
dones,
weights,
sess=self.sess)
if self.prioritized_replay:
new_priorities = np.abs(
td_errors) + self.prioritized_replay_eps
assert isinstance(self.replay_buffer,
PrioritizedReplayBuffer)
self.replay_buffer.update_priorities(
batch_idxes, new_priorities)
if can_sample and self.num_timesteps > self.learning_starts and \
self.num_timesteps % self.target_network_update_freq == 0:
# Update target network periodically.
self.update_target(sess=self.sess)
if step % self.sub_models[0].train_freq == 0:
mb_infos_vals = []
for m in self.sub_models:
# Update policy, critics and target networks
for grad_step in range(m.gradient_steps):
# Break if the warmup phase is not over
# or if there are not enough samples in the replay buffer
if not m.replay_buffer.can_sample(m.batch_size) \
or self.num_timesteps < m.learning_starts:
break
n_updates += 1
# Compute current learning_rate
frac = 1.0 - step / total_timesteps
current_lr = m.learning_rate(frac)
# Update policy and critics (q functions)
mb_infos_vals.append(
m._train_step(step, writer, current_lr))
# Update target network
if (step +
grad_step) % m.target_update_interval == 0:
# Update target network
m.sess.run(m.target_update_op)
if len(episode_rewards[-101:-1]) == 0:
mean_100ep_reward = -np.inf
else:
mean_100ep_reward = round(
float(np.mean(episode_rewards[-101:-1])), 1)
num_episodes = len(episode_rewards)
# print(done, log_interval, len(episode_rewards), self.num_timesteps)
if self.verbose >= 1 and done and log_interval is not None and len(
episode_rewards) % log_interval == 0:
logger.record_tabular("steps", self.num_timesteps)
prev_macro_choices = np.array(prev_macro_choices)
macro_choices_ratio = [
'%.2f' %
((prev_macro_choices[prev_macro_choices == i]).size /
prev_macro_choices.size)
for i in range(self.n_actions)
]
logger.record_tabular("macro choices", macro_choices_ratio)
logger.record_tabular("episodes", num_episodes)
if len(episode_successes) > 0:
logger.logkv("success rate",
np.mean(episode_successes[-100:]))
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward)
logger.record_tabular(
"% time spent exploring",
int(100 * self.exploration.value(self.num_timesteps)))
logger.logkv("n_updates_of_sub", n_updates)
logger.dump_tabular()
print("macro choices", prev_macro_choices)
self.num_timesteps += 1
return self
def eval(self,
total_episodes,
callback=None,
log_interval=100,
tb_log_name="DQN",
reset_num_timesteps=True,
replay_wrapper=None):
new_tb_log = self._init_num_timesteps(reset_num_timesteps)
with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \
as writer:
episode_rewards = [0.0]
episode_successes = []
obs = self.env.reset()
reset = True
macro_count = 0
macro_len = self.macro_len
macro_choices = []
n_updates = 0
macro_action = None
# for step in range(total_timesteps):
while True:
with self.sess.as_default():
if reset or macro_count % macro_len == 0:
# macro_action = self.act(np.array(obs)[None], update_eps=0, **{})[0]
macro_actions, _, _ = self.step_model.step(
np.array(obs)[None], deterministic=False)
macro_action = macro_actions[0]
# macro_action = 1
macro_obs = obs
reward_in_one_macro = 0
macro_count += 1
macro_choices.append(macro_action)
current_sub = self.sub_models[macro_action]
action = current_sub.policy_tf.step(
obs[None], deterministic=True).flatten()
# Add noise to the action (improve exploration,
# not needed in general)
if current_sub.action_noise is not None:
action = np.clip(action + current_sub.action_noise(), -1,
1)
# inferred actions need to be transformed to environment action_space before stepping
unscaled_action = unscale_action(self.env.action_space, action)
assert action.shape == self.env.action_space.shape
reset = False
new_obs, rew, done, info = self.env.step(unscaled_action)
episode_rewards[-1] += rew
rew -= self.args.policy_cost_coef * self.args.sub_policy_costs[
macro_action]
reward_in_one_macro += rew
obs = new_obs
# print("step: %d, done: %d" % (self.num_timesteps, done))
if done:
maybe_is_success = info.get('is_success')
if maybe_is_success is not None:
episode_successes.append(float(maybe_is_success))
if not isinstance(self.env, VecEnv):
obs = self.env.reset()
episode_rewards.append(0.0)
reset = True
macro_action = None
macro_count = 0
print("=" * 70)
print("macro_choices:", macro_choices)
print("return:", episode_rewards[-2])
print("=" * 70)
prev_macro_choices = macro_choices
macro_choices = []
if len(episode_rewards) - 1 == total_episodes:
break
if len(episode_rewards[-101:-1]) == 0:
mean_100ep_reward = -np.inf
else:
mean_100ep_reward = round(
float(np.mean(episode_rewards[-101:-1])), 1)
num_episodes = len(episode_rewards)
# print(done, log_interval, len(episode_rewards), self.num_timesteps)
if self.verbose >= 1 and done and log_interval is not None and len(
episode_rewards) % log_interval == 0:
logger.record_tabular("steps", self.num_timesteps)
logger.record_tabular("macro choices",
np.mean(prev_macro_choices))
logger.record_tabular("episodes", num_episodes)
if len(episode_successes) > 0:
logger.logkv("success rate",
np.mean(episode_successes[-100:]))
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward)
logger.logkv("n_updates_of_sub", n_updates)
logger.dump_tabular()
print("macro choices", prev_macro_choices)
self.num_timesteps += 1
return self
def predict(self,
observation,
state=None,
mask=None,
deterministic=True,
args=None):
observation = np.array(observation)
vectorized_env = self._is_vectorized_observation(
observation, self.observation_space)
observation = observation.reshape((-1, ) +
self.observation_space.shape)
with self.sess.as_default():
if self.macro_count % self.macro_len == 0:
macro_actions, _, _ = self.step_model.step(
observation, deterministic=deterministic)
# macro_actions = self.act(observation, update_eps=0)
self.macro_act = macro_actions[
0] # not supporting vectorized_env
if args.eval_certain_sub != None:
self.macro_act = args.eval_certain_sub
self.macro_count += 1
# Sample from sub_policy
current_sub = self.sub_models[self.macro_act]
action = current_sub.policy_tf.step(observation,
deterministic=deterministic)
action = action.reshape(
(-1, ) +
self.env.action_space.shape) # reshape to the correct action shape
# inferred actions need to be transformed to environment action_space before stepping
unscaled_action = unscale_action(self.env.action_space, action)
unscaled_action = unscaled_action[0] # not supporting vectorized_env
return self.macro_act, unscaled_action, None
def action_probability(self,
observation,
state=None,
mask=None,
actions=None,
logp=False):
observation = np.array(observation)
vectorized_env = self._is_vectorized_observation(
observation, self.observation_space)
observation = observation.reshape((-1, ) +
self.observation_space.shape)
actions_proba = self.proba_step(observation, state, mask)
if actions is not None: # comparing the action distribution, to given actions
actions = np.array([actions])
assert isinstance(self.action_space, gym.spaces.Discrete)
actions = actions.reshape((-1, ))
assert observation.shape[0] == actions.shape[
0], "Error: batch sizes differ for actions and observations."
actions_proba = actions_proba[np.arange(actions.shape[0]), actions]
# normalize action proba shape
actions_proba = actions_proba.reshape((-1, 1))
if logp:
actions_proba = np.log(actions_proba)
if not vectorized_env:
if state is not None:
raise ValueError(
"Error: The environment must be vectorized when using recurrent policies."
)
actions_proba = actions_proba[0]
return actions_proba
def get_parameter_list(self):
return self.params
def save(self, save_path, cloudpickle=False):
# params
data = {
"double_q": self.double_q,
"param_noise": self.param_noise,
"learning_starts": self.learning_starts,
"train_freq": self.train_freq,
"prioritized_replay": self.prioritized_replay,
"prioritized_replay_eps": self.prioritized_replay_eps,
"batch_size": self.batch_size,
"target_network_update_freq": self.target_network_update_freq,
"prioritized_replay_alpha": self.prioritized_replay_alpha,
"prioritized_replay_beta0": self.prioritized_replay_beta0,
"prioritized_replay_beta_iters":
self.prioritized_replay_beta_iters,
"exploration_final_eps": self.exploration_final_eps,
"exploration_fraction": self.exploration_fraction,
"learning_rate": self.learning_rate,
"gamma": self.gamma,
"verbose": self.verbose,
"observation_space": self.observation_space,
"action_space": self.action_space,
"policy": self.policy,
"n_envs": self.n_envs,
"n_cpu_tf_sess": self.n_cpu_tf_sess,
"seed": self.seed,
"_vectorize_action": self._vectorize_action,
"policy_kwargs": self.policy_kwargs
}
params_to_save = self.get_parameters()
self._save_to_file(save_path,
data=data,
params=params_to_save,
cloudpickle=cloudpickle)
class MDPO(OffPolicyRLModel):
"""
Mirror Descent Policy Optimization (MDPO)
Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,
This implementation borrows code from original implementation (https://github.com/haarnoja/sac)
from OpenAI Spinning Up (https://github.com/openai/spinningup) and from the Softlearning repo
(https://github.com/rail-berkeley/softlearning/)
:param policy: (SACPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, LnMlpPolicy, ...)
:param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
:param gamma: (float) the discount factor
:param learning_rate: (float or callable) learning rate for adam optimizer,
the same learning rate will be used for all networks (Q-Values, Actor and Value function)
it can be a function of the current progress (from 1 to 0)
:param buffer_size: (int) size of the replay buffer
:param batch_size: (int) Minibatch size for each gradient update
:param tau: (float) the soft update coefficient ("polyak update", between 0 and 1)
:param lamda: (str or float) Entropy regularization coefficient. (Equivalent to
inverse of reward scale in the original SAC paper.) Controlling exploration/exploitation trade-off.
:param train_freq: (int) Update the model every `train_freq` steps.
:param learning_starts: (int) how many steps of the model to collect transitions for before learning starts
:param target_update_interval: (int) update the target network every `target_network_update_freq` steps.
:param gradient_steps: (int) How many gradient update after each step
:param action_noise: (ActionNoise) the action noise type (None by default), this can help
for hard exploration problem. Cf DDPG for the different action noise type.
:param random_exploration: (float) Probability of taking a random action (as in an epsilon-greedy strategy)
This is not needed for SAC normally but can help exploring when using HER + SAC.
This hack was present in the original OpenAI Baselines repo (DDPG + HER)
:param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
:param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
:param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
:param policy_kwargs: (dict) additional arguments to be passed to the policy on creation
:param full_tensorboard_log: (bool) enable additional logging when using tensorboard
Note: this has no effect on SAC logging for now
"""
def __init__(self,
policy,
env,
gamma=0.99,
learning_rate=3e-4,
buffer_size=50000,
learning_starts=100,
train_freq=1,
batch_size=256,
tau=0.005,
lamda=0,
target_update_interval=1,
gradient_steps=1,
action_noise=None,
random_exploration=0.0,
verbose=0,
tensorboard_log=None,
_init_setup_model=True,
policy_kwargs=None,
full_tensorboard_log=False,
seed=0,
tsallis_q=1,
reparameterize=True,
klconst=1.0):
super(MDPO, self).__init__(policy=policy,
env=env,
replay_buffer=None,
verbose=verbose,
policy_base=SACPolicy,
requires_vec_env=False,
policy_kwargs=policy_kwargs)
self.buffer_size = buffer_size
self.learning_rate = learning_rate
self.learning_starts = learning_starts
self.train_freq = train_freq
self.batch_size = batch_size
self.tau = tau
# In the original paper, same learning rate is used for all networks
# self.policy_lr = learning_rate
# self.qf_lr = learning_rate
# self.vf_lr = learning_rate
# Entropy coefficient / Entropy temperature
# Inverse of the reward scale
self.target_update_interval = target_update_interval
self.gradient_steps = gradient_steps
self.gamma = gamma
self.action_noise = action_noise
self.random_exploration = random_exploration
self.value_fn = None
self.graph = None
self.replay_buffer = None
self.episode_reward = None
self.sess = None
self.tensorboard_log = tensorboard_log
self.verbose = verbose
self.params = None
self.summary = None
self.policy_tf = None
self.full_tensorboard_log = full_tensorboard_log
self.obs_target = None
self.target_policy = None
self.actions_ph = None
self.rewards_ph = None
self.terminals_ph = None
self.observations_ph = None
self.action_target = None
self.next_observations_ph = None
self.value_target = None
self.step_ops = None
self.target_update_op = None
self.infos_names = None
self.entropy = None
self.target_params = None
self.learning_rate_ph = None
self.processed_obs_ph = None
self.processed_next_obs_ph = None
self.seed = seed
self.lamda = lamda
self.tsallis_q = tsallis_q
self.reparameterize = reparameterize
self.klconst = klconst
#if self.tsallis_q == 1.0:
# self.log_type = "log"
#else:
self.log_type = "q-log"
if _init_setup_model:
self.setup_model()
def _get_pretrain_placeholders(self):
policy = self.policy_tf
# Rescale
deterministic_action = self.deterministic_action * np.abs(
self.action_space.low)
return policy.obs_ph, self.actions_ph, deterministic_action
def setup_model(self):
with SetVerbosity(self.verbose):
self.graph = tf.Graph()
with self.graph.as_default():
n_cpu = multiprocessing.cpu_count()
if sys.platform == 'darwin':
n_cpu //= 2
self.sess = tf_util.make_session(num_cpu=n_cpu,
graph=self.graph)
self._setup_learn(self.seed)
self.replay_buffer = ReplayBuffer(self.buffer_size, info=True)
with tf.variable_scope("input", reuse=False):
# Create policy and target TF objects
self.policy_tf = self.policy(self.sess,
self.observation_space,
self.action_space,
**self.policy_kwargs)
self.target_policy = self.policy(self.sess,
self.observation_space,
self.action_space,
**self.policy_kwargs)
self.old_policy = self.policy(self.sess,
self.observation_space,
self.action_space,
**self.policy_kwargs)
# Initialize Placeholders
self.observations_ph = self.policy_tf.obs_ph
# Normalized observation for pixels
self.processed_obs_ph = self.policy_tf.processed_obs
self.next_observations_ph = self.target_policy.obs_ph
self.processed_next_obs_ph = self.target_policy.processed_obs
self.action_target = self.target_policy.action_ph
self.terminals_ph = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals')
self.rewards_ph = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.actions_ph = tf.placeholder(tf.float32,
shape=(None, ) +
self.action_space.shape,
name='actions')
self.learning_rate_ph = tf.placeholder(
tf.float32, [], name="learning_rate_ph")
self.kl_coef_ph = tf.placeholder(tf.float32, [],
name="kl_coef_ph")
with tf.variable_scope("model", reuse=False):
# Create the policy
# first return value corresponds to deterministic actions
# policy_out corresponds to stochastic actions, used for training
# logp_pi is the log probabilty of actions taken by the policy
self.deterministic_action, policy_out, logp_pi, policy_out_unsquashed = self.policy_tf.make_actor( \
self.processed_obs_ph, log_type=self.log_type, q_prime=self.tsallis_q, reparameterize=self.reparameterize)
# Monitor the entropy of the policy,
# this is not used for training
self.entropy = tf.reduce_mean(self.policy_tf.entropy)
# Use two Q-functions to improve performance by reducing overestimation bias.
qf1, qf2, value_fn = self.policy_tf.make_critics(
self.processed_obs_ph,
self.actions_ph,
create_qf=True,
create_vf=True)
qf1_pi, qf2_pi, _ = self.policy_tf.make_critics(
self.processed_obs_ph,
policy_out,
create_qf=True,
create_vf=False,
reuse=True)
with tf.variable_scope("target", reuse=False):
# Create the value network
_, _, value_target = self.target_policy.make_critics(
self.processed_next_obs_ph,
create_qf=False,
create_vf=True)
self.value_target = value_target
with tf.variable_scope("old", reuse=False):
_, _, logp_pi_old, _ = self.old_policy.make_actor( \
self.processed_obs_ph, action=policy_out, action_unsquashed=policy_out_unsquashed, log_type=self.log_type, q_prime=self.tsallis_q, reparameterize=self.reparameterize)
qf1_pi_old, _, _ = self.old_policy.make_critics(
self.processed_obs_ph,
policy_out,
create_qf=True,
create_vf=False)
with tf.variable_scope("loss", reuse=False):
# Take the min of the two Q-Values (Double-Q Learning)
min_qf_pi = tf.minimum(qf1_pi, qf2_pi)
# Target for Q value regression
q_backup = tf.stop_gradient(1 * self.rewards_ph +
(1 - self.terminals_ph) *
self.gamma * self.value_target)
# Compute Q-Function loss
# TODO: test with huber loss (it would avoid too high values)
qf1_loss = 0.5 * tf.reduce_mean((q_backup - qf1)**2)
qf2_loss = 0.5 * tf.reduce_mean((q_backup - qf2)**2)
# Compute the policy loss
# Alternative: policy_kl_loss = tf.reduce_mean(logp_pi - min_qf_pi)
if self.reparameterize:
policy_kl_loss = tf.reduce_mean(
logp_pi * self.kl_coef_ph + self.tsallis_q *
(self.lamda - self.kl_coef_ph) * logp_pi_old -
qf1_pi)
else:
target_policy = tf.clip_by_value(
tf.exp(
(-logp_old * self.lamda + qf1_pi - value_fn) /
(self.kl_coef_ph + self.lamda)), 0, 0.9)
policy_kl_loss = tf.reduce_mean(
target_policy *
(target_policy - logp_pi_old_action))
mean_reg_loss = 1e-3 * tf.reduce_mean(self.policy_tf.act_mu
**2)
std_reg_loss = 1e-3 * tf.reduce_mean(self.policy_tf.logstd
**2)
#pre_tanh_value = policy_outputs[-1]
#pre_activation_reg_loss = self.policy_pre_activation_weight * (
# (pre_tanh_value**2).sum(dim=1).mean()
#)
policy_reg_loss = mean_reg_loss + std_reg_loss #+ pre_activation_reg_loss
# NOTE: in the original implementation, they have an additional
# regularization loss for the gaussian parameters
# this is not used for now
# policy_loss = (policy_kl_loss + policy_regularization_loss)
policy_loss = policy_kl_loss + policy_reg_loss
# Target for value fn regression
# We update the vf towards the min of two Q-functions in order to
# reduce overestimation bias from function approximation error.
v_backup = tf.stop_gradient(min_qf_pi -
self.lamda * logp_pi)
value_loss = 0.5 * tf.reduce_mean((value_fn - v_backup)**2)
values_losses = qf1_loss + qf2_loss + value_loss
# Policy train op
# (has to be separate from value train op, because min_qf_pi appears in policy_loss)
#policy_optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate_ph)
policy_optimizer = tf.train.AdamOptimizer(
learning_rate=3e-4)
policy_train_op = policy_optimizer.minimize(
policy_loss, var_list=get_vars('model/pi'))
# Value train op
#value_optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate_ph)
value_optimizer = tf.train.AdamOptimizer(
learning_rate=3e-4)
values_params = get_vars('model/values_fn')
source_params = get_vars("model/values_fn/vf")
target_params = get_vars("target/values_fn/vf")
# Polyak averaging for target variables
self.target_update_op = [
tf.assign(target,
(1 - self.tau) * target + self.tau * source)
for target, source in zip(target_params, source_params)
]
# Initializing target to match source variables
target_init_op = [
tf.assign(target, source)
for target, source in zip(target_params, source_params)
]
curr_params = get_vars("model/pi") + get_vars(
"model/values_fn/qf1")
old_params = get_vars("old/pi") + get_vars(
"old/values_fn/qf1")
self.assign_policy_op = [
tf.assign(
old, curr
) #(1 - self.gradient_steps) * old + self.gradient_steps * curr)
for old, curr in zip(old_params, curr_params)
]
# Control flow is used because sess.run otherwise evaluates in nondeterministic order
# and we first need to compute the policy action before computing q values losses
with tf.control_dependencies([policy_train_op]):
train_values_op = value_optimizer.minimize(
values_losses, var_list=values_params)
self.infos_names = [
'policy_loss', 'qf1_loss', 'qf2_loss',
'value_loss', 'entropy'
]
# All ops to call during one training step
self.step_ops = [
policy_loss, qf1_loss, qf2_loss, value_loss, qf1,
qf2, value_fn, logp_pi, self.entropy, logp_pi_old,
train_values_op, policy_train_op
]
# Monitor losses and entropy in tensorboard
tf.summary.scalar('policy_loss', policy_loss)
tf.summary.scalar('qf1_loss', qf1_loss)
tf.summary.scalar('qf2_loss', qf2_loss)
tf.summary.scalar('value_loss', value_loss)
tf.summary.scalar('entropy', self.entropy)
tf.summary.scalar('learning_rate',
tf.reduce_mean(self.learning_rate_ph))
# Retrieve parameters that must be saved
self.params = get_vars("model")
self.target_params = get_vars("target/values_fn/vf")
# Initialize Variables and target network
with self.sess.as_default():
self.sess.run(tf.global_variables_initializer())
self.sess.run(target_init_op)
self.summary = tf.summary.merge_all()
def _train_step(self, step, learning_rate, kl_coef):
# Sample a batch from the replay buffer
batch = self.replay_buffer.sample(self.batch_size)
batch_obs, batch_actions, batch_rewards, batch_next_obs, batch_dones, batch_terminals = batch
feed_dict = {
self.observations_ph: batch_obs,
self.actions_ph: batch_actions,
self.next_observations_ph: batch_next_obs,
self.rewards_ph: batch_rewards.reshape(self.batch_size, -1),
self.terminals_ph: batch_terminals.reshape(self.batch_size, -1),
self.learning_rate_ph: learning_rate,
self.kl_coef_ph: kl_coef
}
# Do one gradient step
out = self.sess.run(self.step_ops, feed_dict)
# Unpack to monitor losses and entropy
policy_loss, qf1_loss, qf2_loss, value_loss, *values = out
# qf1, qf2, value_fn, logp_pi, entropy, *_ = values
entropy = values[4]
return policy_loss, qf1_loss, qf2_loss, value_loss, entropy
def learn(self,
total_timesteps,
callback=None,
seed=None,
log_interval=2000,
tb_log_name="MDPO",
reset_num_timesteps=True,
replay_wrapper=None):
new_tb_log = self._init_num_timesteps(reset_num_timesteps)
if replay_wrapper is not None:
self.replay_buffer = replay_wrapper(self.replay_buffer)
with SetVerbosity(self.verbose):
# Transform to callable if needed
self.learning_rate = get_schedule_fn(self.learning_rate)
# Initial learning rate
current_lr = self.learning_rate(1)
frac = 0
t_k = 0
start_time = time.time()
episode_rewards = [0.0]
episode_successes = []
if self.action_noise is not None:
self.action_noise.reset()
obs = self.env.reset()
self.episode_reward = np.zeros((1, ))
ep_info_buf = deque(maxlen=100)
n_updates = 0
infos_values = []
for step in range(total_timesteps):
if callback is not None:
# Only stop training if return value is False, not when it is None. This is for backwards
# compatibility with callbacks that have no return statement.
if callback(locals(), globals()) is False:
break
# Before training starts, randomly sample actions
# from a uniform distribution for better exploration.
# Afterwards, use the learned policy
# if random_exploration is set to 0 (normal setting)
if (self.num_timesteps < self.learning_starts
or np.random.rand() < self.random_exploration):
# No need to rescale when sampling random action
rescaled_action = action = self.env.action_space.sample()
else:
action = self.policy_tf.step(obs[None],
deterministic=True).flatten()
#action = self.policy_tf.step(obs[None], deterministic=True).flatten()
# Add noise to the action (improve exploration,
# not needed in general)
if self.action_noise is not None:
action = np.clip(action + self.action_noise(), -1, 1)
# Rescale from [-1, 1] to the correct bounds
rescaled_action = action * np.abs(self.action_space.low)
assert action.shape == self.env.action_space.shape
#print("action", action)
new_obs, reward, done, info = self.env.step(rescaled_action)
#print("new obs", new_obs)
# Store transition in the replay buffer.
self.replay_buffer.add(obs, action, reward, new_obs,
float(done), info)
obs = new_obs
# Retrieve reward and episode length if using Monitor wrapper
maybe_ep_info = info.get('episode')
if maybe_ep_info is not None:
ep_info_buf.extend([maybe_ep_info])
if step % self.train_freq == 0:
mb_infos_vals = []
# Update policy, critics and target networks
for grad_step in range(1): #int(self.gradient_steps)
# Break if the warmup phase is not over
# or if there are not enough samples in the replay buffer
if not self.replay_buffer.can_sample(self.batch_size) \
or self.num_timesteps < self.learning_starts:
break
n_updates += 1
# Compute current learning_rate
t_k = self.klconst # step / total_timesteps
frac = 1.0 - step / total_timesteps
current_lr = self.learning_rate(frac)
# Update policy and critics (q functions)
mb_infos_vals.append(
self._train_step(step, current_lr, t_k))
# Update target network
if (step +
grad_step) % self.target_update_interval == 0:
# Update target network
self.sess.run(self.target_update_op)
# Log losses and entropy, useful for monitor training
if len(mb_infos_vals) > 0:
infos_values = np.mean(mb_infos_vals, axis=0)
if step % self.gradient_steps == 0:
self.sess.run(self.assign_policy_op)
episode_rewards[-1] += reward
if done:
if self.action_noise is not None:
self.action_noise.reset()
if not isinstance(self.env, VecEnv):
obs = self.env.reset()
episode_rewards.append(0.0)
maybe_is_success = info.get('is_success')
if maybe_is_success is not None:
episode_successes.append(float(maybe_is_success))
if len(episode_rewards[-101:-1]) == 0:
mean_reward = -np.inf
else:
mean_reward = round(
float(np.mean(episode_rewards[-101:-1])), 1)
num_episodes = len(episode_rewards)
self.num_timesteps += 1
# Display training infos
if self.verbose >= 1 and step % log_interval == 0:
fps = int(step / (time.time() - start_time))
logger.logkv("episodes", num_episodes)
logger.logkv("mean 100 episode reward", mean_reward)
if len(ep_info_buf) > 0 and len(ep_info_buf[0]) > 0:
logger.logkv(
'ep_rewmean',
safe_mean(
[ep_info['r'] for ep_info in ep_info_buf]))
logger.logkv(
'eplenmean',
safe_mean(
[ep_info['l'] for ep_info in ep_info_buf]))
logger.logkv("n_updates", n_updates)
logger.logkv("current_lr", current_lr)
logger.logkv("lamda", self.lamda)
logger.logkv("fps", fps)
logger.logkv('time_elapsed', int(time.time() - start_time))
if len(episode_successes) > 0:
logger.logkv("success rate",
np.mean(episode_successes[-100:]))
if len(infos_values) > 0:
for (name, val) in zip(self.infos_names, infos_values):
logger.logkv(name, val)
logger.logkv("total timesteps", self.num_timesteps)
logger.logkv("t_k", t_k)
logger.logkv("steps", step)
logger.dumpkvs()
# Reset infos:
infos_values = []
return self
def action_probability(self,
observation,
state=None,
mask=None,
actions=None,
logp=False):
if actions is not None:
raise ValueError("Error: MDPO does not have action probabilities.")
warnings.warn(
"Even though MDPO has a Gaussian policy, it cannot return a distribution as it "
"is squashed by a tanh before being scaled and ouputed.")
return None
def predict(self, observation, state=None, mask=None, deterministic=True):
observation = np.array(observation)
vectorized_env = self._is_vectorized_observation(
observation, self.observation_space)
observation = observation.reshape((-1, ) +
self.observation_space.shape)
actions = self.policy_tf.step(observation, deterministic=deterministic)
actions = actions.reshape(
(-1, ) +
self.action_space.shape) # reshape to the correct action shape
actions = actions * np.abs(
self.action_space.low) # scale the output for the prediction
if not vectorized_env:
actions = actions[0]
return actions, None
def get_parameter_list(self):
return (self.params + self.target_params)
def save(self, save_path):
data = {
"learning_rate": self.learning_rate,
"buffer_size": self.buffer_size,
"learning_starts": self.learning_starts,
"train_freq": self.train_freq,
"batch_size": self.batch_size,
"tau": self.tau,
"kl_coef": self.kl_coef_ph,
# Should we also store the replay buffer?
# this may lead to high memory usage
# with all transition inside
# "replay_buffer": self.replay_buffer
"gamma": self.gamma,
"verbose": self.verbose,
"observation_space": self.observation_space,
"action_space": self.action_space,
"policy": self.policy,
"n_envs": self.n_envs,
"action_noise": self.action_noise,
"random_exploration": self.random_exploration,
"_vectorize_action": self._vectorize_action,
"policy_kwargs": self.policy_kwargs
}
params_to_save = self.get_parameters()
self._save_to_file(save_path, data=data, params=params_to_save)
class DQN(OffPolicyRLModel):
"""
The DQN model class. DQN paper: https://arxiv.org/pdf/1312.5602.pdf
:param policy: (DQNPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, LnMlpPolicy, ...)
:param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
:param gamma: (float) discount factor
:param learning_rate: (float) learning rate for adam optimizer
:param buffer_size: (int) size of the replay buffer
:param exploration_fraction: (float) fraction of entire training period over which the exploration rate is
annealed
:param exploration_final_eps: (float) final value of random action probability
:param train_freq: (int) update the model every `train_freq` steps. set to None to disable printing
:param batch_size: (int) size of a batched sampled from replay buffer for training
:param checkpoint_freq: (int) how often to save the model. This is so that the best version is restored at the
end of the training. If you do not wish to restore the best version
at the end of the training set this variable to None.
:param checkpoint_path: (str) replacement path used if you need to log to somewhere else than a temporary
directory.
:param learning_starts: (int) how many steps of the model to collect transitions for before learning starts
:param target_network_update_freq: (int) update the target network every `target_network_update_freq` steps.
:param prioritized_replay: (bool) if True prioritized replay buffer will be used.
:param prioritized_replay_alpha: (float)alpha parameter for prioritized replay buffer.
It determines how much prioritization is used, with alpha=0 corresponding to the uniform case.
:param prioritized_replay_beta0: (float) initial value of beta for prioritized replay buffer
:param prioritized_replay_beta_iters: (int) number of iterations over which beta will be annealed from initial
value to 1.0. If set to None equals to max_timesteps.
:param prioritized_replay_eps: (float) epsilon to add to the TD errors when updating priorities.
:param param_noise: (bool) Whether or not to apply noise to the parameters of the policy.
:param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
:param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
:param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
:param full_tensorboard_log: (bool) enable additional logging when using tensorboard
WARNING: this logging can take a lot of space quickly
"""
def __init__(self,
policy,
env,
gamma=0.99,
learning_rate=5e-4,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
target_network_update_freq=500,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
verbose=0,
tensorboard_log=None,
_init_setup_model=True,
policy_kwargs=None,
full_tensorboard_log=False):
# TODO: replay_buffer refactoring
super(DQN, self).__init__(policy=policy,
env=env,
replay_buffer=None,
verbose=verbose,
policy_base=DQNPolicy,
requires_vec_env=False,
policy_kwargs=policy_kwargs)
self.checkpoint_path = checkpoint_path
self.param_noise = param_noise
self.learning_starts = learning_starts
self.train_freq = train_freq
self.prioritized_replay = prioritized_replay
self.prioritized_replay_eps = prioritized_replay_eps
self.batch_size = batch_size
self.target_network_update_freq = target_network_update_freq
self.checkpoint_freq = checkpoint_freq
self.prioritized_replay_alpha = prioritized_replay_alpha
self.prioritized_replay_beta0 = prioritized_replay_beta0
self.prioritized_replay_beta_iters = prioritized_replay_beta_iters
self.exploration_final_eps = exploration_final_eps
self.exploration_fraction = exploration_fraction
self.buffer_size = buffer_size
self.learning_rate = learning_rate
self.gamma = gamma
self.tensorboard_log = tensorboard_log
self.full_tensorboard_log = full_tensorboard_log
self.graph = None
self.sess = None
self._train_step = None
self.step_model = None
self.update_target = None
self.act = None
self.proba_step = None
self.replay_buffer = None
self.beta_schedule = None
self.exploration = None
self.params = None
self.summary = None
self.episode_reward = None
if _init_setup_model:
self.setup_model()
def _get_pretrain_placeholders(self):
policy = self.step_model
return policy.obs_ph, tf.placeholder(tf.int32, [None]), policy.q_values
@staticmethod
def _is_vectorized_observation(observation, observation_space):
"""
For every observation type, detects and validates the shape,
then returns whether or not the observation is vectorized.
:param observation: (np.ndarray) the input observation to validate
:param observation_space: (gym.spaces) the observation space
:return: (bool) whether the given observation is vectorized or not
"""
if isinstance(observation_space, gym.spaces.Box):
if observation.shape == observation_space.shape:
return False
elif observation.shape[1:] == observation_space.shape:
return True
else:
raise ValueError(
"Error: Unexpected observation shape {} for ".format(
observation.shape) +
"Box environment, please use {} ".format(
observation_space.shape) +
"or (n_env, {}) for the observation shape.".format(
", ".join(map(str, observation_space.shape))))
elif isinstance(observation_space, gym.spaces.Discrete):
if observation.shape == (
): # A numpy array of a number, has shape empty tuple '()'
return False
elif len(observation.shape) == 1:
return True
else:
raise ValueError(
"Error: Unexpected observation shape {} for ".format(
observation.shape) +
"Discrete environment, please use (1,) or (n_env, 1) for the observation shape."
)
elif isinstance(observation_space, gym.spaces.MultiDiscrete):
if observation.shape == (len(observation_space.nvec), ):
return False
elif len(observation.shape) == 2 and observation.shape[1] == len(
observation_space.nvec):
return True
else:
raise ValueError(
"Error: Unexpected observation shape {} for MultiDiscrete "
.format(observation.shape) +
"environment, please use ({},) or ".format(
len(observation_space.nvec)) +
"(n_env, {}) for the observation shape.".format(
len(observation_space.nvec)))
elif isinstance(observation_space, gym.spaces.MultiBinary):
if observation.shape == (observation_space.n, ):
return False
elif len(observation.shape
) == 2 and observation.shape[1] == observation_space.n:
return True
else:
raise ValueError(
"Error: Unexpected observation shape {} for MultiBinary ".
format(observation.shape) +
"environment, please use ({},) or ".format(
observation_space.n) +
"(n_env, {}) for the observation shape.".format(
observation_space.n))
elif isinstance(observation_space, gym.spaces.Dict):
import logging
# print("the observation is the space type {} "
# "and the observation is in vector with shape {}.".format(observation_space, observation.shape))
pass
else:
raise ValueError(
"Error: Cannot determine if the observation is vectorized with the space type {}."
.format(observation_space))
def setup_model(self):
with SetVerbosity(self.verbose):
assert not isinstance(self.action_space, gym.spaces.Box), \
"Error: DQN cannot output a gym.spaces.Box action space."
# If the policy is wrap in functool.partial (e.g. to disable dueling)
# unwrap it to check the class type
if isinstance(self.policy, partial):
test_policy = self.policy.func
else:
test_policy = self.policy
assert issubclass(test_policy, DQNPolicy), "Error: the input policy for the DQN model must be " \
"an instance of DQNPolicy."
self.graph = tf.Graph()
with self.graph.as_default():
self.sess = tf_util.make_session(graph=self.graph)
optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate)
self.act, self._train_step, self.update_target, self.step_model = deepq.build_train(
q_func=partial(self.policy, **self.policy_kwargs),
ob_space=self.observation_space,
ac_space=self.action_space,
optimizer=optimizer,
gamma=self.gamma,
grad_norm_clipping=10,
param_noise=self.param_noise,
sess=self.sess,
full_tensorboard_log=self.full_tensorboard_log)
self.proba_step = self.step_model.proba_step
self.params = tf_util.get_trainable_vars("deepq")
# Initialize the parameters and copy them to the target network.
tf_util.initialize(self.sess)
self.update_target(sess=self.sess)
self.summary = tf.summary.merge_all()
def learn(self,
total_timesteps,
callback=None,
seed=None,
log_interval=100,
tb_log_name="DQN",
reset_num_timesteps=True,
replay_wrapper=None,
learning_curve=False,
test_t=None):
new_tb_log = self._init_num_timesteps(reset_num_timesteps)
with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \
as writer:
self._setup_learn(seed)
# Create the replay buffer
if self.prioritized_replay:
self.replay_buffer = PrioritizedReplayBuffer(
self.buffer_size, alpha=self.prioritized_replay_alpha)
if self.prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
else:
prioritized_replay_beta_iters = self.prioritized_replay_beta_iters
self.beta_schedule = LinearSchedule(
prioritized_replay_beta_iters,
initial_p=self.prioritized_replay_beta0,
final_p=1.0)
else:
self.replay_buffer = ReplayBuffer(self.buffer_size)
self.beta_schedule = None
if replay_wrapper is not None:
assert not self.prioritized_replay, "Prioritized replay buffer is not supported by HER"
self.replay_buffer = replay_wrapper(self.replay_buffer)
# Create the schedule for exploration starting from 1.
self.exploration = LinearSchedule(
schedule_timesteps=int(self.exploration_fraction *
total_timesteps),
initial_p=1.0,
final_p=self.exploration_final_eps)
episode_rewards = [0.0]
self.cumul_reward = [0.0]
episode_successes = []
obs = self.env.reset()
reset = True
self.episode_reward = np.zeros((1, ))
# variables for test eval ##
test_step = test_t * 3
test_results = {'sum': []}
test_ts = []
for _ in range(total_timesteps):
## Test eval period ##
if learning_curve and _ % test_step == 0 and _ > 0:
print("--> Simulating test period")
self.env.reset()
test_r = 0.0
for i in range(test_t):
feasible_actions = AllocationEnv.get_feasible_actions(
obs["board_config"])
action_mask = AllocationEnv.get_action_mask(
feasible_actions, self.env.action_space.n)
action, _states = self.predict(obs, mask=action_mask)
action = AllocationEnv.check_action(
obs['board_config'], action)
obs, rewards, dones, info = self.env.step(action)
test_r += rewards
test_results["sum"].append(test_r)
test_ts.append(_)
self.env.reset()
# plot test eval progress
plt.plot(test_ts, test_results["sum"])
# plt.errorbar(iteration_cuts, results["mean"], yerr=results["std"], fmt='.k')
plt.xlabel("Iteration count")
plt.ylabel("Total (sum) test reward")
plt.savefig("figs/rl-learning-curve-{}.pdf".format(
cfg.vals['prj_name']))
plt.clf()
plt.close()
# write test eval progress
write_results = {}
for k, v in test_results.items():
write_results[k] = serialize_floats(v)
with open(
"output/rl-learning-curve-{}.json".format(
cfg.vals['prj_name']), 'w') as f:
json.dump(write_results, f)
if callback is not None:
# Only stop training if return value is False, not when it is None. This is for backwards
# compatibility with callbacks that have no return statement.
if callback(locals(), globals()) is False:
break
# Take action and update exploration to the newest value
kwargs = {}
if not self.param_noise:
update_eps = self.exploration.value(self.num_timesteps)
update_param_noise_threshold = 0.
else:
update_eps = 0.
# Compute the threshold such that the KL divergence between perturbed and non-perturbed
# policy is comparable to eps-greedy exploration with eps = exploration.value(t).
# See Appendix C.1 in Parameter Space Noise for Exploration, Plappert et al., 2017
# for detailed explanation.
update_param_noise_threshold = \
-np.log(1. - self.exploration.value(self.num_timesteps) +
self.exploration.value(self.num_timesteps) / float(self.env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
feasible_actions = AllocationEnv.get_feasible_actions(
obs["board_config"])
action_mask = AllocationEnv.get_action_mask(
feasible_actions, self.action_space.n)
with self.sess.as_default():
action = self.act(State.get_vec_observation(obs)[None],
update_eps=update_eps,
**kwargs,
mask=action_mask)[0]
reset = False
# CHECK IF ACTIONS IS FEASIBLE
action = AllocationEnv.check_action(obs['board_config'],
action)
env_action = action
new_obs, rew, done, info = self.env.step(env_action)
print("action: {} - reward: {} - eps: {:.4}".format(
action, rew, update_eps))
print(new_obs['day_vec'])
print(new_obs['board_config'])
# Store transition in the replay buffer.
self.replay_buffer.add(State.get_vec_observation(obs), action,
rew, State.get_vec_observation(new_obs),
float(done))
obs = new_obs
if writer is not None:
ep_rew = np.array([rew]).reshape((1, -1))
ep_done = np.array([done]).reshape((1, -1))
self.episode_reward = total_episode_reward_logger(
self.episode_reward, ep_rew, ep_done, writer,
self.num_timesteps)
episode_rewards[-1] += rew
self.cumul_reward.append(self.cumul_reward[-1] + rew)
if done:
maybe_is_success = info.get('is_success')
if maybe_is_success is not None:
episode_successes.append(float(maybe_is_success))
if not isinstance(self.env, VecEnv):
obs = self.env.reset()
episode_rewards.append(0.0)
reset = True
# Do not train if the warmup phase is not over
# or if there are not enough samples in the replay buffer
can_sample = self.replay_buffer.can_sample(self.batch_size)
if can_sample and self.num_timesteps > self.learning_starts \
and self.num_timesteps % self.train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if self.prioritized_replay:
experience = self.replay_buffer.sample(
self.batch_size,
beta=self.beta_schedule.value(self.num_timesteps))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = self.replay_buffer.sample(
self.batch_size)
weights, batch_idxes = np.ones_like(rewards), None
if writer is not None:
# run loss backprop with summary, but once every 100 steps save the metadata
# (memory, compute time, ...)
if (1 + self.num_timesteps) % 100 == 0:
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary, td_errors = self._train_step(
obses_t,
actions,
rewards,
obses_tp1,
obses_tp1,
dones,
weights,
sess=self.sess,
options=run_options,
run_metadata=run_metadata)
writer.add_run_metadata(
run_metadata, 'step%d' % self.num_timesteps)
else:
summary, td_errors = self._train_step(
obses_t,
actions,
rewards,
obses_tp1,
obses_tp1,
dones,
weights,
sess=self.sess)
writer.add_summary(summary, self.num_timesteps)
else:
_, td_errors = self._train_step(obses_t,
actions,
rewards,
obses_tp1,
obses_tp1,
dones,
weights,
sess=self.sess)
if self.prioritized_replay:
new_priorities = np.abs(
td_errors) + self.prioritized_replay_eps
self.replay_buffer.update_priorities(
batch_idxes, new_priorities)
if can_sample and self.num_timesteps > self.learning_starts and \
self.num_timesteps % self.target_network_update_freq == 0:
# Update target network periodically.
self.update_target(sess=self.sess)
if len(episode_rewards[-101:-1]) == 0:
mean_100ep_reward = -np.inf
else:
mean_100ep_reward = round(
float(np.mean(episode_rewards[-101:-1])), 1)
num_episodes = len(episode_rewards)
if self.verbose >= 1 and done and log_interval is not None and len(
episode_rewards) % log_interval == 0:
logger.record_tabular("steps", self.num_timesteps)
logger.record_tabular("episodes", num_episodes)
if len(episode_successes) > 0:
logger.logkv("success rate",
np.mean(episode_successes[-100:]))
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward)
logger.record_tabular(
"% time spent exploring",
int(100 * self.exploration.value(self.num_timesteps)))
logger.dump_tabular()
print('timestamp: {}'.format(self.num_timesteps, end='\r\n'))
self.num_timesteps += 1
return self
def predict(self, observation, state=None, mask=None, deterministic=True):
if isinstance(observation, dict):
observation = State.get_vec_observation(observation)[None]
vectorized_env = self._is_vectorized_observation(
observation, self.observation_space)
with self.sess.as_default():
actions, _, _ = self.step_model.step(observation,
deterministic=deterministic,
mask=mask)
if not vectorized_env:
actions = actions[0]
return actions, None
def action_probability(self,
observation,
state=None,
mask=None,
actions=None,
logp=False):
observation = np.array(observation)
vectorized_env = self._is_vectorized_observation(
observation, self.observation_space)
observation = observation.reshape((-1, ) +
self.observation_space.shape)
actions_proba = self.proba_step(observation, state, mask)
if actions is not None: # comparing the action distribution, to given actions
actions = np.array([actions])
assert isinstance(self.action_space, gym.spaces.Discrete)
actions = actions.reshape((-1, ))
assert observation.shape[0] == actions.shape[
0], "Error: batch sizes differ for actions and observations."
actions_proba = actions_proba[np.arange(actions.shape[0]), actions]
# normalize action proba shape
actions_proba = actions_proba.reshape((-1, 1))
if logp:
actions_proba = np.log(actions_proba)
if not vectorized_env:
if state is not None:
raise ValueError(
"Error: The environment must be vectorized when using recurrent policies."
)
actions_proba = actions_proba[0]
return actions_proba
def get_parameter_list(self):
return self.params
def save(self, save_path):
# params
data = {
"checkpoint_path": self.checkpoint_path,
"param_noise": self.param_noise,
"learning_starts": self.learning_starts,
"train_freq": self.train_freq,
"prioritized_replay": self.prioritized_replay,
"prioritized_replay_eps": self.prioritized_replay_eps,
"batch_size": self.batch_size,
"target_network_update_freq": self.target_network_update_freq,
"checkpoint_freq": self.checkpoint_freq,
"prioritized_replay_alpha": self.prioritized_replay_alpha,
"prioritized_replay_beta0": self.prioritized_replay_beta0,
"prioritized_replay_beta_iters":
self.prioritized_replay_beta_iters,
"exploration_final_eps": self.exploration_final_eps,
"exploration_fraction": self.exploration_fraction,
"learning_rate": self.learning_rate,
"gamma": self.gamma,
"verbose": self.verbose,
"observation_space": self.observation_space,
"action_space": self.action_space,
"policy": self.policy,
"n_envs": self.n_envs,
"_vectorize_action": self._vectorize_action,
"policy_kwargs": self.policy_kwargs
}
params_to_save = self.get_parameters()
self._save_to_file(save_path, data=data, params=params_to_save)
def update_weights(self, buffer):
obses_t, actions, rewards, obses_tp1, dones = buffer.sample(
self.batch_size)
weights, batch_idxes = np.ones_like(rewards), None
_, td_errors = self._train_step(obses_t,
actions,
rewards,
obses_tp1,
obses_tp1,
dones,
weights,
sess=self.sess)
def learn_off_policy(self, total_timesteps, buffer):
print("Off-policy training")
for i in tqdm(range(total_timesteps)):
self.update_weights(buffer)
