def save_act(self, path=None):
"""Save model to a pickle located at `path`"""
if path is None:
path = os.path.join(logger.get_dir(), "model.pkl")
with tempfile.TemporaryDirectory() as td:
save_variables(os.path.join(td, "model"))
arc_name = os.path.join(td, "packed.zip")
with zipfile.ZipFile(arc_name, 'w') as zipf:
for root, dirs, files in os.walk(td):
for fname in files:
file_path = os.path.join(root, fname)
if file_path != arc_name:
zipf.write(file_path, os.path.relpath(file_path, td))
with open(arc_name, "rb") as f:
model_data = f.read()
with open(path, "wb") as f:
cloudpickle.dump((model_data, self._act_params), f)
def train_policy(arglist):
with U.single_threaded_session():
# Create the environment
if arglist.use_dense_rewards:
print("Will use env MineRLNavigateDense-v0")
env = gym.make("MineRLNavigateDense-v0")
env_name = "MineRLNavigateDense-v0"
else:
print("Will use env MineRLNavigate-v0")
env = gym.make('MineRLNavigate-v0')
env_name = "MineRLNavigate-v0"
if arglist.force_forward:
env = MineCraftWrapperSimplified(env)
else:
env = MineCraftWrapper(env)
if not arglist.use_demonstrations:
# Use stack of last 4 frames as obs
env = FrameStack(env, 4)
# Create all the functions necessary to train the model
act, train, update_target, debug = deepq.build_train(
make_obs_ph=lambda name: ObservationInput(env.observation_space,
name=name),
q_func=build_q_func('conv_only', dueling=True),
num_actions=env.action_space.n,
gamma=0.9,
optimizer=tf.train.AdamOptimizer(learning_rate=5e-4),
)
# Create the replay buffer(s) (TODO: Use prioritized replay buffer)
if arglist.use_demonstrations:
replay_buffer = ReplayBuffer(int(arglist.replay_buffer_len / 2))
demo_buffer = load_demo_buffer(env_name,
int(arglist.replay_buffer_len / 2))
else:
replay_buffer = ReplayBuffer(arglist.replay_buffer_len)
# Create the schedule for exploration starting from 1 (every action is random) down to
# 0.02 (98% of actions are selected according to values predicted by the model).
exploration = LinearSchedule(
schedule_timesteps=arglist.num_exploration_steps *
arglist.num_episodes * arglist.max_episode_steps,
initial_p=1.0,
final_p=arglist.final_epsilon)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
n_episodes = 0
n_steps = 0
obs = env.reset()
log_path = "./learning_curves/minerl_" + str(date.today()) + "_" + str(
time.time()) + ".dat"
log_file = open(log_path, "a")
for episode in range(arglist.num_episodes):
print("Episode: ", str(episode))
done = False
episode_steps = 0
while not done:
# Take action and update exploration to the newest value
action = act(obs[None],
update_eps=exploration.value(n_steps))[0]
new_obs, rew, done, _ = env.step(action)
n_steps += 1
episode_steps += 1
# Break episode
if episode_steps > arglist.max_episode_steps:
done = True
# Store transition in the replay buffer.
replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
# Store rewards
episode_rewards[-1] += rew
if done:
obs = env.reset()
episode_rewards.append(0)
n_episodes += 1
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if (n_steps > arglist.learning_starts_at_steps) and (n_steps %
4 == 0):
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(
32)
train(obses_t, actions, rewards, obses_tp1, dones,
np.ones_like(rewards))
if arglist.use_demonstrations:
if (n_steps < arglist.learning_starts_at_steps) and (
n_steps % 4 == 0):
obses_t, actions, rewards, obses_tp1, dones = demo_buffer.sample(
32)
train(obses_t, actions, rewards, obses_tp1, dones,
np.ones_like(rewards))
if (n_steps > arglist.learning_starts_at_steps) and (
n_steps % 4 == 0):
obses_t, actions, rewards, obses_tp1, dones = demo_buffer.sample(
32)
train(obses_t, actions, rewards, obses_tp1, dones,
np.ones_like(rewards))
# Update target network periodically.
if n_steps % arglist.target_net_update_freq == 0:
update_target()
# Log data for analysis
if done and len(episode_rewards) % 10 == 0:
logger.record_tabular("steps", n_steps)
logger.record_tabular("episodes", len(episode_rewards))
logger.record_tabular(
"mean episode reward",
round(np.mean(episode_rewards[-101:-1]), 1))
logger.record_tabular(
"% time spent exploring",
int(100 * exploration.value(n_steps)))
logger.dump_tabular()
#TODO: Save checkpoints
if n_steps % arglist.checkpoint_rate == 0:
checkpoint_path = "./checkpoints/minerl_" + str(
episode) + "_" + str(date.today()) + "_" + str(
time.time()) + ".pkl"
save_variables(checkpoint_path)
print("%s,%s,%s,%s" %
(n_steps, episode,
round(np.mean(episode_rewards[-101:-1]),
1), int(100 * exploration.value(n_steps))),
file=log_file)
log_file.close()
def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=100000,
exploration_fraction=0.1,
exploration_final_eps=0.1,
train_freq=1,
batch_size=64,
print_freq=1,
eval_freq=2500,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
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,
callback=None,
load_path=None,
csv_path="results.csv",
method_type="baseline",
**network_kwargs):
"""Train a deepr model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepr.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
batch_size: int
size of a batch sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
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.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
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 total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepr/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
#q_func = build_q_func(network, **network_kwargs)
q_func = build_q_func(mlp(num_layers=4, num_hidden=64), **network_kwargs)
#q_func = build_q_func(mlp(num_layers=2, num_hidden=64, activation=tf.nn.relu), **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size,
alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(
schedule_timesteps=int(exploration_fraction * total_timesteps),
#initial_p=1.0,
initial_p=exploration_final_eps,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
eval_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
reset = True
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
csvfile = open(csv_path, 'w', newline='')
fieldnames = ['STEPS', 'REWARD']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for t in range(total_timesteps + 1):
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
#update_eps = exploration.value(t)
update_eps = exploration_final_eps
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. - exploration.value(
t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
action_mask = get_mask(env, method_type)
a = act(np.array(obs)[None],
unused_actions_neginf_mask=action_mask,
update_eps=update_eps,
**kwargs)[0]
env_action = a
reset = False
new_obs, rew, done, _ = env.step(env_action)
eval_rewards[-1] += rew
action_mask_p = get_mask(env, method_type)
# Shaping
if method_type == 'shaping':
## look-ahead shaping
ap = act(np.array(new_obs)[None],
unused_actions_neginf_mask=action_mask_p,
stochastic=False)[0]
f = action_mask_p[ap] - action_mask[a]
rew = rew + f
# Store transition in the replay buffer.
#replay_buffer.add(obs, a, rew, new_obs, float(done), action_mask_p)
if method_type != 'shaping':
replay_buffer.add(obs, a, rew, new_obs, float(done),
np.zeros(env.action_space.n))
else:
replay_buffer.add(obs, a, rew, new_obs, float(done),
action_mask_p)
obs = new_obs
if t % eval_freq == 0:
eval_rewards.append(0.0)
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(
batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones, masks_tp1 = replay_buffer.sample(
batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, actions, rewards, obses_tp1, dones,
weights, masks_tp1)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes,
new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
mean_eval_reward = round(np.mean(eval_rewards[-1 - print_freq:-1]),
1)
num_evals = len(eval_rewards)
if t > 0 and t % eval_freq == 0 and print_freq is not None and t % (
print_freq * eval_freq) == 0:
#if done and print_freq is not None and len(eval_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("evals", num_evals)
logger.record_tabular("average reward in this eval",
mean_eval_reward / (eval_freq))
logger.record_tabular("total reward in this eval",
mean_eval_reward)
logger.dump_tabular()
writer.writerow({
"STEPS": t,
"REWARD": mean_eval_reward / (eval_freq)
})
csvfile.flush()
if (checkpoint_freq is not None and t > learning_starts
and num_evals > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_eval_reward > saved_mean_reward:
if print_freq is not None:
logger.log(
"Saving model due to mean reward increase: {} -> {}"
.format(saved_mean_reward, mean_eval_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_eval_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(
saved_mean_reward))
load_variables(model_file)
return act
def save(self, save_path):
tf_util.save_variables(save_path)
def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=5,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
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,
callback=None,
load_path=None,
**network_kwargs):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
batch_size: int
size of a batch sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
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.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
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 total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the trained model from. (default: None)(used in test stage)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
med_libs = MedLibs()
'''Define Q network
inputs: observation place holder(make_obs_ph), num_actions, scope, reuse
outputs(tensor of shape batch_size*num_actions): values of each action, Q(s,a_{i})
'''
q_func = build_q_func(network, **network_kwargs)
''' To put observations into a placeholder '''
# TODO: Can only deal with Discrete and Box observation spaces for now
# observation_space = env.observation_space (default)
# Use sub_obs_space instead
observation_space = med_libs.subobs_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
''' Customize action '''
# TODO: subset of action space.
action_dim = med_libs.sub_act_dim
'''
Returns: deepq.build_train()
act: (tf.Variable, bool, float) -> tf.Variable
function to select and action given observation.
act is computed by [build_act] or [build_act_with_param_noise]
train: (object, np.array, np.array, object, np.array, np.array) -> np.array
optimize the error in Bellman's equation.
update_target: () -> ()
copy the parameters from optimized Q function to the target Q function.
debug: {str: function}
a bunch of functions to print debug data like q_values.
'''
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=action_dim,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
double_q=True,
grad_norm_clipping=10,
param_noise=param_noise)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': action_dim,
}
'''Contruct an act object using ActWrapper'''
act = ActWrapper(act, act_params)
''' Create the replay buffer'''
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size,
alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
'''Create the schedule for exploration starting from 1.'''
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
'''
Initialize all the uninitialized variables in the global scope and copy them to the target network.
'''
U.initialize()
update_target()
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
sub_obs = med_libs.custom_obs(obs) # TODO: customize observations
pre_obs = obs
reset = True
mydict = med_libs.action_dict
already_starts = False
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
# load_path: a trained model/policy
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
''' Training loop starts'''
t = 0
while t < total_timesteps:
if callback is not None:
if callback(locals(), globals()):
break
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
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).
update_param_noise_threshold = -np.log(1. - exploration.value(
t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
''' Choose action: take action and update exploration to the newest value
'''
# TODO: Mixed action strategy
# Normal status, action is easily determined by rules, use [obs]
action = med_libs.simple_case_action(obs)
# Distraction status, action is determined by Q, with [sub_obs]
if action == -10:
action = act(np.array(sub_obs)[None],
update_eps=update_eps,
**kwargs)[0]
action = med_libs.action_Q_env(
action
) # TODO:action_Q_env, from Q_action(0~2) to env_action(2~4)
reset = False
''' Step action '''
new_obs, rew, done, d_info = env.step(action)
d_att_last = int(pre_obs[0][0])
d_att_now = int(obs[0][0])
d_att_next = int(new_obs[0][0])
''' Store transition in the replay buffer.'''
pre_obs = obs
obs = new_obs
sub_new_obs = med_libs.custom_obs(new_obs)
if (d_att_last == 0 and d_att_now == 1) and not already_starts:
already_starts = True
if already_starts and d_att_now == 1:
replay_buffer.add(sub_obs, action, rew, sub_new_obs,
float(done))
episode_rewards[-1] += rew # Sum of rewards
t = t + 1
print(
'>> Iteration:{}, State[d_att,cd_activate,L4_available,ssl4_activate,f_dc]:{}'
.format(t, sub_obs))
print(
'Dis_Last:{}, Dis_Now:{}, Dis_Next:{},Reward+Cost:{}, Action:{}'
.format(
d_att_last, d_att_now, d_att_next, rew,
list(mydict.keys())[list(
mydict.values()).index(action)]))
# update sub_obs
sub_obs = sub_new_obs
# Done and Reset
if done:
print('Done infos: ', d_info)
print('======= end =======')
obs = env.reset()
sub_obs = med_libs.custom_obs(obs) # TODO: custom obs
pre_obs = obs # TODO: save obs at t-1
already_starts = False
episode_rewards.append(0.0)
reset = True
# Update the Q network parameters
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(
batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(
batch_size)
weights, batch_idxes = np.ones_like(rewards), None
# Calculate td-errors
actions = med_libs.action_env_Q(
actions
) # TODO:action_env_Q, from env_action(2~4) to Q_action(0~2)
td_errors = train(obses_t, actions, rewards, obses_tp1, dones,
weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes,
new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically, copy weights of Q to target Q
update_target()
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(
episode_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward)
logger.record_tabular("% time spent exploring",
int(100 * exploration.value(t)))
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts
and num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log(
"Saving model due to mean reward increase: {} -> {}"
.format(saved_mean_reward, mean_100ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(
saved_mean_reward))
load_variables(model_file)
return act
def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
lambda_=0.1,
margin=0.1,
i_before=1,
gamma=1.0,
target_network_update_freq=500,
double_q=False,
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,
callback=None,
load_path=None,
**network_kwargs
):
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = modified_deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
lambda_=lambda_,
margin=margin,
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise,
double_q=double_q
)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = ModifiedPrioritizedReplayBuffer(buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ModifiedReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction * total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
episode_scores = deque(maxlen=100)
saved_mean_reward = None
obs = env.reset()
reset = True
trained = False
num_steps_per_episode = 0
obses_before = deque(maxlen=i_before)
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
for t in range(total_timesteps):
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
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. - exploration.value(t) + exploration.value(t) / float(env.action_space.n)
)
kwargs['reset'] = reset
kwargs['update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
action = act(np.array(obs)[None], update_eps=update_eps, **kwargs)[0]
env_action = action
reset = False
new_obs, rew, done, info = env.step(env_action)
# Store transition in the replay buffer.
if len(obses_before) < obses_before.maxlen:
obses_before.append(obs)
obs_tmi = np.zeros_like(obs)
replay_buffer.new_add(obs_tmi, obs, action, rew, new_obs, float(done), float(False))
else:
obs_tmi = obses_before.popleft()
obses_before.append(obs)
replay_buffer.new_add(obs_tmi, obs, action, rew, new_obs, float(done), float(True))
obs = new_obs
episode_rewards[-1] += rew
num_steps_per_episode += 1
if done:
if 'episode' in info:
episode_scores.append(info['episode']['r'])
obs = env.reset()
episode_rewards.append(0.0)
reset = True
obses_before.clear()
if t > learning_starts and t % train_freq == 0:
trained = True
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(batch_size, beta=beta_schedule.value(t))
obses_tmi, obses_t, actions, rewards, obses_tp1, dones, has_obs_tmis, weights, batch_idxes = experience
else:
obses_tmi, obses_t, actions, rewards, obses_tp1, dones, has_obs_tmis = replay_buffer.sample(
batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors, min_delta_d, max_delta_d, delta_d, representation_loss, weighted_error = train(
obses_tmi, obses_t, actions, rewards, obses_tp1, dones, has_obs_tmis, weights
)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes, new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
if len(episode_scores) != 0:
mean_100ep_scores = sum(episode_scores) / len(episode_scores)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(episode_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward", mean_100ep_reward)
logger.record_tabular("mean 100 episode scores", mean_100ep_scores)
logger.record_tabular("number of steps per episode", num_steps_per_episode)
logger.record_tabular("% time spent exploring", int(100 * exploration.value(t)))
if t > learning_starts and trained:
# Log extra loss information
logger.record_tabular("delta d", delta_d)
logger.record_tabular("min delta d", min_delta_d)
logger.record_tabular("max delta d", max_delta_d)
logger.record_tabular("representation loss", representation_loss)
logger.record_tabular("weighted error", weighted_error)
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts and
num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log("Saving model due to mean reward increase: {} -> {}".format(
saved_mean_reward, mean_100ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
if done:
num_steps_per_episode = 0
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(saved_mean_reward))
load_variables(model_file)
return act
def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
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,
callback=None,
load_path=None,
**network_kwargs
):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
set to None to disable printing
batch_size: int
size of a batched sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
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.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
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 total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise
)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction * total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
reset = True
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
for t in range(total_timesteps):
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
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. - exploration.value(t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs['update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
action = act(np.array(obs)[None], update_eps=update_eps, **kwargs)[0]
env_action = action
reset = False
new_obs, rew, done, _ = env.step(env_action)
# Store transition in the replay buffer.
replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
episode_rewards[-1] += rew
if done:
obs = env.reset()
episode_rewards.append(0.0)
reset = True
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights, batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, actions, rewards, obses_tp1, dones, weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes, new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(episode_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward", mean_100ep_reward)
logger.record_tabular("% time spent exploring", int(100 * exploration.value(t)))
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts and
num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log("Saving model due to mean reward increase: {} -> {}".format(
saved_mean_reward, mean_100ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(saved_mean_reward))
load_variables(model_file)
return act
def learn_neural_linear(
env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=10, #100
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=999,
gamma=1.0,
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,
callback=None,
load_path=None,
ddqn=False,
prior="no prior",
actor="dqn",
**network_kwargs):
#Train a deepq model.
# Create all the functions necessary to train the model
checkpoint_path = logger.get_dir()
sess = get_session()
set_global_seeds(seed)
blr_params = BLRParams()
q_func = deepq.models.cnn_to_mlp(
convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
hiddens=[blr_params.feat_dim],
dueling=bool(0),
)
# q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, feat_dim, feat, feat_target, target, last_layer_weights, blr_ops, blr_helpers = deepq.build_train_neural_linear(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise,
double_q=ddqn,
actor=actor)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size,
alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
reset = True
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
# BLR
# preliminearies
num_actions = env.action_space.n
w_mu = np.zeros((num_actions, feat_dim))
w_sample = np.random.normal(loc=0,
scale=0.1,
size=(num_actions, feat_dim))
w_target = np.random.normal(loc=0,
scale=0.1,
size=(num_actions, feat_dim))
w_cov = np.zeros((num_actions, feat_dim, feat_dim))
for a in range(num_actions):
w_cov[a] = np.eye(feat_dim)
phiphiT = np.zeros((num_actions, feat_dim, feat_dim))
phiY = np.zeros((num_actions, feat_dim))
a0 = 6
b0 = 6
a_sig = [a0 for _ in range(num_actions)]
b_sig = [b0 for _ in range(num_actions)]
yy = [0 for _ in range(num_actions)]
blr_update = 0
for t in tqdm(range(total_timesteps)):
if callback is not None:
if callback(locals(), globals()):
break
# if t % 1000 == 0:
# print("{}/{}".format(t,total_timesteps))
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
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. - exploration.value(
t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
action = act(np.array(obs)[None], w_sample[None])
env_action = action
reset = False
new_obs, rew, done, _ = env.step(env_action)
# clipping like in BDQN
rew = np.sign(rew)
# Store transition in the replay buffer.
replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
episode_rewards[-1] += rew
if done:
obs = env.reset()
episode_rewards.append(0.0)
reset = True
# sample new w from posterior
if t > 0 and t % blr_params.sample_w == 0:
for i in range(num_actions):
if blr_params.no_prior:
w_sample[i] = np.random.multivariate_normal(
w_mu[i], w_cov[i])
else:
sigma2_s = b_sig[i] * invgamma.rvs(a_sig[i])
w_sample[i] = np.random.multivariate_normal(
w_mu[i], sigma2_s * w_cov[i])
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(
batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(
batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, actions, rewards, obses_tp1, dones,
weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes,
new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
# when target network updates we update our posterior belifes
# and transfering information from the old target
# to our new target
blr_update += 1
if blr_update == 10: #10
print("updating posterior parameters")
if blr_params.no_prior:
phiphiT, phiY, w_mu, w_cov, a_sig, b_sig = BayesRegNoPrior(
phiphiT, phiY, w_target, replay_buffer, feat,
feat_target, target, num_actions,
blr_params, w_mu, w_cov,
sess.run(last_layer_weights), prior, blr_ops,
blr_helpers)
else:
phiphiT, phiY, w_mu, w_cov, a_sig, b_sig = BayesRegWithPrior(
phiphiT, phiY, w_target, replay_buffer, feat,
feat_target, target, num_actions, blr_params, w_mu,
w_cov, sess.run(last_layer_weights))
blr_update = 0
print("updateing target, steps {}".format(t))
update_target()
w_target = w_mu
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
mean_10ep_reward = round(np.mean(episode_rewards[-11:-1]), 1)
num_episodes = len(episode_rewards)
# if done and print_freq is not None and len(episode_rewards) % print_freq == 0:
if t % 10000 == 0: #1000
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward)
logger.record_tabular("mean 10 episode reward",
mean_10ep_reward)
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts
and num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log(
"Saving model due to mean reward increase: {} -> {}"
.format(saved_mean_reward, mean_100ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(
saved_mean_reward))
load_variables(model_file)
return act
def save(self, save_path):
tf_util.save_variables(save_path)
def learn(env,
network,
seed=None,
lr=1e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
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,
multiplayer=False,
callback=None,
load_path=None,
load_path_1=None,
load_path_2=None,
**network_kwargs):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
set to None to disable printing
batch_size: int
size of a batched sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
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.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
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 total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# This was all handled in not the most elegant way
# Variables have a _1 or _2 appended to them to separate them
# and a bunch of if statementss to have the _2 variables not do anything in single-player
# when in multiplayer Space Invaders, need to not reward players for other player dying
isSpaceInvaders = False
if "SpaceInvaders" in str(env):
isSpaceInvaders = True
# put a limit on the amount of memory used, otherwise TensorFlow will consume nearly everything
# this leaves 1 GB free on my computer, others may need to change it
# Create all the functions necessary to train the model
# Create two separate TensorFlow sessions
graph_1 = tf.Graph()
sess_1 = tf.Session(graph=graph_1)
if multiplayer:
graph_2 = tf.Graph()
sess_2 = tf.Session(graph=graph_2)
else:
# set session 2 to None if it's not being used
sess_2 = None
set_global_seeds(seed)
# specify the q functions as separate objects
q_func_1 = build_q_func(network, **network_kwargs)
if multiplayer:
q_func_2 = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
# build everything for the first model
# pass in the session and the "_1" suffix
act_1, train_1, update_target_1, debug_1 = deepq.build_train(
sess=sess_1,
make_obs_ph=make_obs_ph,
q_func=q_func_1,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise,
scope="deepq")
# a lot of if multiplayer statements duplicating these actions for a second network
# pass in session 2 and "_2" instead
if multiplayer:
act_2, train_2, update_target_2, debug_2 = deepq.build_train(
sess=sess_2,
make_obs_ph=make_obs_ph,
q_func=q_func_2,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise,
scope="deepq")
# separate act_params for each wrapper
act_params_1 = {
'make_obs_ph': make_obs_ph,
'q_func': q_func_1,
'num_actions': env.action_space.n,
}
if multiplayer:
act_params_2 = {
'make_obs_ph': make_obs_ph,
'q_func': q_func_2,
'num_actions': env.action_space.n,
}
# make the act wrappers
act_1 = ActWrapper(act_1, act_params_1)
if multiplayer:
act_2 = ActWrapper(act_2, act_params_2)
# I need to return something if it's single-player
else:
act_2 = None
# Create the replay buffer
# separate replay buffers are required for each network
# this is required for competitive because the replay buffers hold rewards
# and player 2 has different rewards than player 1
if prioritized_replay:
replay_buffer_1 = PrioritizedReplayBuffer(
buffer_size, alpha=prioritized_replay_alpha)
if multiplayer:
replay_buffer_2 = PrioritizedReplayBuffer(
buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer_1 = ReplayBuffer(buffer_size)
if multiplayer:
replay_buffer_2 = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
# initialize both sessions
U.initialize(sess_1)
if multiplayer:
U.initialize(sess_2)
# the session was passed into these functions when they were created
# the separate update functions work within the different sessions
update_target_1()
if multiplayer:
update_target_2()
# keep track of rewards for both models separately
episode_rewards_1 = [0.0]
saved_mean_reward_1 = None
if multiplayer:
episode_rewards_2 = [0.0]
saved_mean_reward_2 = None
obs = env.reset()
reset = True
# storing stuff in a temporary directory while it's working
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file_1 = os.path.join(td, "model_1")
temp_file_1 = os.path.join(td, "temp_1")
model_saved_1 = False
if multiplayer:
model_file_2 = os.path.join(td, "model_2")
temp_file_2 = os.path.join(td, "temp_2")
model_saved_2 = False
if tf.train.latest_checkpoint(td) is not None:
if multiplayer:
# load both models if multiplayer is on
load_variables(model_file_1, sess_1)
logger.log('Loaded model 1 from {}'.format(model_file_1))
model_saved_1 = True
load_variables(model_file_2, sess_2)
logger.log('Loaded model 2 from {}'.format(model_file_2))
model_saved_2 = True
# otherwise just load the first one
else:
load_variables(model_file_1, sess_1)
logger.log('Loaded model from {}'.format(model_file_1))
model_saved_1 = True
# I have separate load variables for single-player and multiplayer
# this should be None if multiplayer is on
elif load_path is not None:
load_variables(load_path, sess_1)
logger.log('Loaded model from {}'.format(load_path))
# load the separate models in for multiplayer
# should load the variables into the appropriate sessions
# my format may restrict things to working properly only when a Player 1 model is loaded into session 1, and same for Player 2
# however, in practice, the models won't work properly otherwise
elif multiplayer:
if load_path_1 is not None:
load_variables(load_path_1, sess_1)
logger.log('Loaded model 1 from {}'.format(load_path_1))
if load_path_2 is not None:
load_variables(load_path_2, sess_2)
logger.log('Loaded model 2 from {}'.format(load_path_2))
# actual training starts here
for t in range(total_timesteps):
# use this for updating purposes
actual_t = t + 1
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
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. - exploration.value(
t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
# receive model 1's action based on the model and observation
action_1 = act_1(np.array(obs)[None],
update_eps=update_eps,
**kwargs)[0]
env_action_1 = action_1
# do the same for model 2 if in multiplayer
if multiplayer:
action_2 = act_2(np.array(obs)[None],
update_eps=update_eps,
**kwargs)[0]
env_action_2 = action_2
reset = False
# apply actions to the environment
if multiplayer:
new_obs, rew_1, rew_2, done, _ = env.step(
env_action_1, env_action_2)
# apply single action if there isn't a second model
else:
new_obs, rew_1, rew_2, done, _ = env.step(env_action_1)
# manual clipping for Space Invaders multiplayer
if isSpaceInvaders and multiplayer:
# don't reward a player when the other player dies
# change the reward to 0
# the only time either player will get rewarded 200 is when the other player dies
if rew_1 >= 200:
rew_1 = rew_1 - 200.0
if rew_2 >= 200:
rew_2 = rew_2 - 200.0
# manually clip the rewards using the sign function
rew_1 = np.sign(rew_1)
rew_2 = np.sign(rew_2)
combo_factor = 0.25
rew_1_combo = rew_1 + combo_factor * rew_2
rew_2_combo = rew_2 + combo_factor * rew_1
rew_1 = rew_1_combo
rew_2 = rew_2_combo
# Store transition in the replay buffers
replay_buffer_1.add(obs, action_1, rew_1, new_obs, float(done))
if multiplayer:
# pass reward_2 to the second player
# this reward will vary based on the game
replay_buffer_2.add(obs, action_2, rew_2, new_obs, float(done))
obs = new_obs
# separate rewards for each model
episode_rewards_1[-1] += rew_1
if multiplayer:
episode_rewards_2[-1] += rew_2
if done:
obs = env.reset()
episode_rewards_1.append(0.0)
if multiplayer:
episode_rewards_2.append(0.0)
reset = True
if actual_t > learning_starts and actual_t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
# sample from the two replay buffers
if prioritized_replay:
experience_1 = replay_buffer_1.sample(
batch_size, beta=beta_schedule.value(t))
(obses_t_1, actions_1, rewards_1, obses_tp1_1, dones_1,
weights_1, batch_idxes_1) = experience_1
# keep all the variables with separate names
if multiplayer:
experience_2 = replay_buffer_2.sample(
batch_size, beta=beta_schedule.value(t))
(obses_t_2, actions_2, rewards_2, obses_tp1_2, dones_2,
weights_2, batch_idxes_2) = experience_2
# do the same if there's no prioritization
else:
obses_t_1, actions_1, rewards_1, obses_tp1_1, dones_1 = replay_buffer_1.sample(
batch_size)
weights_1, batch_idxes_1 = np.ones_like(rewards_1), None
if multiplayer:
obses_t_2, actions_2, rewards_2, obses_tp1_2, dones_2 = replay_buffer_2.sample(
batch_size)
weights_2, batch_idxes_2 = np.ones_like(
rewards_2), None
# actually train the model based on the samples
td_errors_1 = train_1(obses_t_1, actions_1, rewards_1,
obses_tp1_1, dones_1, weights_1)
if multiplayer:
td_errors_2 = train_2(obses_t_2, actions_2, rewards_2,
obses_tp1_2, dones_2, weights_2)
# give new priority weights to the observations
if prioritized_replay:
new_priorities_1 = np.abs(
td_errors_1) + prioritized_replay_eps
replay_buffer_1.update_priorities(batch_idxes_1,
new_priorities_1)
if multiplayer:
new_priorities_2 = np.abs(
td_errors_2) + prioritized_replay_eps
replay_buffer_2.update_priorities(
batch_idxes_2, new_priorities_2)
if actual_t > learning_starts and actual_t % target_network_update_freq == 0:
# Update target networks periodically.
update_target_1()
if multiplayer:
update_target_2()
# this section is for the purposes of logging stuff
# calculate the average reward over the last 100 episodes
mean_100ep_reward_1 = round(np.mean(episode_rewards_1[-101:-1]), 1)
if multiplayer:
mean_100ep_reward_2 = round(
np.mean(episode_rewards_2[-101:-1]), 1)
num_episodes = len(episode_rewards_1)
# every given number of episodes log and print out the appropriate stuff
if done and print_freq is not None and len(
episode_rewards_1) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
# print out both rewards if multiplayer
if multiplayer:
logger.record_tabular("mean 100 episode reward 1",
mean_100ep_reward_1)
logger.record_tabular("mean 100 episode reward 2",
mean_100ep_reward_2)
else:
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward_1)
logger.record_tabular("% time spent exploring",
int(100 * exploration.value(t)))
logger.dump_tabular()
# save best-performing version of each model
# I've opted out of this for competitive multiplayer because it's difficult to determine what's "best"
if (checkpoint_freq is not None and actual_t > learning_starts
and num_episodes > 100
and actual_t % checkpoint_freq == 0):
# if there's a best reward, save it as the new best model
if saved_mean_reward_1 is None or mean_100ep_reward_1 > saved_mean_reward_1:
if print_freq is not None:
if multiplayer:
logger.log(
"Saving model 1 due to mean reward increase: {} -> {}"
.format(saved_mean_reward_1,
mean_100ep_reward_1))
else:
logger.log(
"Saving model due to mean reward increase: {} -> {}"
.format(saved_mean_reward_1,
mean_100ep_reward_1))
save_variables(model_file_1, sess_1)
model_saved_1 = True
saved_mean_reward_1 = mean_100ep_reward_1
if multiplayer and (saved_mean_reward_2 is None or
mean_100ep_reward_2 > saved_mean_reward_2):
if print_freq is not None:
logger.log(
"Saving model 2 due to mean reward increase: {} -> {}"
.format(saved_mean_reward_2, mean_100ep_reward_2))
save_variables(model_file_2, sess_2)
model_saved_2 = True
saved_mean_reward_2 = mean_100ep_reward_2
# restore models at the end to the best performers
if model_saved_1:
if print_freq is not None:
logger.log("Restored model 1 with mean reward: {}".format(
saved_mean_reward_1))
load_variables(model_file_1, sess_1)
if multiplayer and model_saved_2:
if print_freq is not None:
logger.log("Restored model 2 with mean reward: {}".format(
saved_mean_reward_2))
load_variables(model_file_2, sess_2)
return act_1, act_2, sess_1, sess_2
def train(train_params, policy_params, env_id):
# Refresh training progress log
logger._configure_default_logger()
from baselines.ppo1 import mlp_policy, pposgd_simple
U.make_session(num_cpu=1).__enter__()
def policy_fn(name, ob_space, ac_space):
return mlp_policy.MlpPolicy(
name=name, ob_space=ob_space, ac_space=ac_space, # set tensor
hid_size=policy_params.nodes_per_layer, # set nodes
num_hid_layers=policy_params.num_layers) # set layers
# Set up environment
env = gym.make(env_id)
env = RewScale(env, 0.1)
# Seed Set
rank = MPI.COMM_WORLD.Get_rank()
workerseed = train_params.seed + 1000000 * rank
env.seed(workerseed)
print('----------=================--------------')
print('rank: ', rank, 'workerseed: ', workerseed)
print('----------=================--------------')
# Run Training with stochastic gradient descent
pi = pposgd_simple.learn(env, policy_fn,
max_timesteps=train_params.num_timesteps,
timesteps_per_actorbatch=train_params.timesteps_per_actorbatch,
clip_param=train_params.clip_param,
entcoeff=train_params.entcoeff,
optim_epochs=train_params.optim_epochs,
optim_stepsize=train_params.optim_stepsize,
optim_batchsize=train_params.optim_batchsize,
gamma=train_params.gamma,
lam=train_params.lam,
schedule=train_params.schedule,
)
env.close
# Save Trained Model an meta data
print(train_params.model_path)
if train_params.model_path:
# Make Dir
model_log_dir=os.environ['GYMFC_EXP_MODELSDIR']+train_params.model_name
os.makedirs(train_params.model_dir, exist_ok=True)
# Merge Metadata
meta_data = {**vars(train_params), **vars(policy_params)}
# Save Metadata as csv
md_file = train_params.model_dir+'/'+'metadata.csv'
md_keys = meta_data.keys()
try:
with open(md_file, 'w') as mdfile:
writer = csv.DictWriter(mdfile, fieldnames = md_keys)
writer.writeheader()
writer.writerow(meta_data)
except IOError:
print("I/O error")
# Save Model
U.save_variables(train_params.model_path)
# Save Training Progress file
log_path = os.environ['OPENAI_LOGDIR']+'progress.csv' # train prog log
copyfile(log_path, train_params.model_dir+'/'+'log.csv') # copy log csv
else:
print('model not named')
return pi
def save(self, path, sess):
save_variables(path, sess)
def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=1000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
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,
callback=None,
load_path=None,
save_path=None,
**network_kwargs):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
batch_size: int
size of a batch sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
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.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
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 total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
logger = logging.getLogger()
coloredlogs.install(
level='DEBUG',
fmt=
'%(asctime)s,%(msecs)03d %(filename)s[%(process)d] %(levelname)s %(message)s'
)
logger.setLevel(logging.DEBUG)
# DATAVAULT: Set up list of action meanings and two lists to store episode
# and total sums for each possible action in the list.
action_names = env.unwrapped.get_action_meanings()
action_episode_sums = []
action_total_sums = []
for x in range(len(action_names)):
action_episode_sums.append(0)
action_total_sums.append(0)
# And obviously, you need a datavault item
dv = DataVault()
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size,
alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
reset = True
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
#DATAVAULT: This is where you usually want to scrape data - in the timestep loop
for t in range(total_timesteps):
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
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. - exploration.value(
t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
# if environment is pacman, limit moves to four directions
name = env.unwrapped.spec.id
if name == "MsPacmanNoFrameskip-v4":
while True:
step_return = act(np.array(obs)[None],
update_eps=update_eps,
**kwargs)
action = step_return[0][0]
env_action = action
q_values = np.squeeze(step_return[1])
# test for break condition
if 1 <= action <= 4:
break
else:
step_return = act(np.array(obs)[None],
update_eps=update_eps,
**kwargs)
action = step_return[0][0]
q_values = np.squeeze(step_return[1])
env_action = action
reset = False
new_obs, rew, done, info = env.step(env_action)
# DATAVAULT: after each step, we push the information out to the datavault
lives = env.ale.lives()
#store_data(self, action, action_name, action_episode_sums, action_total_sums, reward, done, info, lives, q_values, observation, mean_reward):
action_episode_sums, action_total_sums = dv.store_data(
action, action_names[action], action_episode_sums,
action_total_sums, rew, done, info, lives, q_values, new_obs,
saved_mean_reward)
# Store transition in the replay buffer.
replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
episode_rewards[-1] += rew
if done:
obs = env.reset()
episode_rewards.append(0.0)
reset = True
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(
batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(
batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, actions, rewards, obses_tp1, dones,
weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes,
new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
if (len(episode_rewards[-101:-1]) > 0):
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
else:
mean_100ep_reward = 0
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(
episode_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward)
logger.record_tabular("% time spent exploring",
int(100 * exploration.value(t)))
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts
and num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log(
"Saving model due to mean reward increase: {} -> {}"
.format(saved_mean_reward, mean_100ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(
saved_mean_reward))
load_variables(model_file)
dv.make_dataframes()
print("Save path is: ")
print(save_path)
# use parent dir to save data, so we can keep the current folder small and portable
directory = os.path.abspath(os.path.join(save_path, os.pardir))
csv_path = os.path.join(directory, 'CSVs')
os.mkdir(csv_path)
dv.df_to_csv(csv_path)
return act
def save(self, save_path):
U.save_variables(save_path)
def save(self, path):
save_variables(path)
def do_agent_exploration(updates_queue: multiprocessing.Queue,
q_func_vars_trained_queue: multiprocessing.Queue,
network, seed, config, lr, total_timesteps,
learning_starts, buffer_size, exploration_fraction,
exploration_initial_eps, exploration_final_eps,
train_freq, batch_size, print_freq, checkpoint_freq,
gamma, target_network_update_freq, prioritized_replay,
prioritized_replay_alpha, prioritized_replay_beta0,
prioritized_replay_beta_iters, prioritized_replay_eps,
experiment_name, load_path, network_kwargs):
env = DotaEnvironment()
sess = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, _, _, debug = deepq.build_train(
scope='deepq_act',
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
total_timesteps),
initial_p=exploration_initial_eps,
final_p=exploration_final_eps)
U.initialize()
reward_shaper = ActionAdviceRewardShaper(config=config)
reward_shaper.load()
reward_shaper.generate_merged_demo()
full_exp_name = '{}-{}'.format(date.today().strftime('%Y%m%d'),
experiment_name)
experiment_dir = os.path.join('experiments', full_exp_name)
os.makedirs(experiment_dir, exist_ok=True)
summary_dir = os.path.join(experiment_dir, 'summaries')
os.makedirs(summary_dir, exist_ok=True)
summary_writer = tf.summary.FileWriter(summary_dir)
checkpoint_dir = os.path.join(experiment_dir, 'checkpoints')
os.makedirs(checkpoint_dir, exist_ok=True)
stats_dir = os.path.join(experiment_dir, 'stats')
os.makedirs(stats_dir, exist_ok=True)
with tempfile.TemporaryDirectory() as td:
td = checkpoint_dir or td
os.makedirs(td, exist_ok=True)
model_file = os.path.join(td, "best_model")
model_saved = False
saved_mean_reward = None
# if os.path.exists(model_file):
# print('Model is loading')
# load_variables(model_file)
# logger.log('Loaded model from {}'.format(model_file))
# model_saved = True
# elif load_path is not None:
# load_variables(load_path)
# logger.log('Loaded model from {}'.format(load_path))
def synchronize_q_func_vars():
updates_queue.put(
UpdateMessage(UPDATE_STATUS_SEND_WEIGHTS, None, None))
q_func_vars_trained = q_func_vars_trained_queue.get()
update_q_func_expr = []
for var, var_trained in zip(debug['q_func_vars'],
q_func_vars_trained):
update_q_func_expr.append(var.assign(var_trained))
update_q_func_expr = tf.group(*update_q_func_expr)
sess.run(update_q_func_expr)
synchronize_q_func_vars()
episode_rewards = []
act_step_t = 0
while act_step_t < total_timesteps:
# Reset the environment
obs = env.reset()
obs = StatePreprocessor.process(obs)
episode_rewards.append(0.0)
done = False
# Demo preservation variables
demo_picked = 0
demo_picked_step = 0
# Demo switching statistics
demo_switching_stats = [(0, 0)]
# Sample the episode until it is completed
act_started_step_t = act_step_t
while not done:
# Take action and update exploration to the newest value
biases, demo_indexes = reward_shaper.get_action_potentials_with_indexes(
obs, act_step_t)
update_eps = exploration.value(act_step_t)
actions, is_randoms = act(np.array(obs)[None],
biases,
update_eps=update_eps)
action, is_random = actions[0], is_randoms[0]
if not is_random:
bias_demo = demo_indexes[action]
if bias_demo != demo_switching_stats[-1][1]:
demo_switching_stats.append(
(act_step_t - act_started_step_t, bias_demo))
if bias_demo != 0 and demo_picked == 0:
demo_picked = bias_demo
demo_picked_step = act_step_t + 1
pairs = env.step(action)
action, (new_obs, rew, done, _) = pairs[-1]
logger.log(
f'{act_step_t}/{total_timesteps} obs {obs} action {action}'
)
# Compute state on the real reward but learn from the normalized version
episode_rewards[-1] += rew
rew = np.sign(rew) * np.log(1 + np.abs(rew))
new_obs = StatePreprocessor.process(new_obs)
if len(new_obs) == 0:
done = True
else:
transition = (obs, action, rew, new_obs, float(done),
act_step_t)
obs = new_obs
act_step_t += 1
if act_step_t - demo_picked_step >= MIN_STEPS_TO_FOLLOW_DEMO_FOR:
demo_picked = 0
reward_shaper.set_demo_picked(act_step_t, demo_picked)
updates_queue.put(
UpdateMessage(UPDATE_STATUS_CONTINUE, transition,
demo_picked))
# Post episode logging
summary = tf.Summary(value=[
tf.Summary.Value(tag="rewards",
simple_value=episode_rewards[-1])
])
summary_writer.add_summary(summary, act_step_t)
summary = tf.Summary(
value=[tf.Summary.Value(tag="eps", simple_value=update_eps)])
summary_writer.add_summary(summary, act_step_t)
summary = tf.Summary(value=[
tf.Summary.Value(tag="episode_steps",
simple_value=act_step_t - act_started_step_t)
])
summary_writer.add_summary(summary, act_step_t)
mean_5ep_reward = round(float(np.mean(episode_rewards[-5:])), 1)
num_episodes = len(episode_rewards)
if print_freq is not None and num_episodes % print_freq == 0:
logger.record_tabular("steps", act_step_t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 5 episode reward", mean_5ep_reward)
logger.record_tabular("% time spent exploring",
int(100 * exploration.value(act_step_t)))
logger.dump_tabular()
# Wait for the learning to finish and synchronize
synchronize_q_func_vars()
# Record demo_switching_stats
if num_episodes % 10 == 0:
save_demo_switching_stats(demo_switching_stats, stats_dir,
num_episodes)
if checkpoint_freq is not None and num_episodes % checkpoint_freq == 0:
# Periodically save the model
rec_model_file = os.path.join(
td, "model_{}_{:.2f}".format(num_episodes,
mean_5ep_reward))
save_variables(rec_model_file)
# Check whether the model is the best so far
if saved_mean_reward is None or mean_5ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log(
"Saving model due to mean reward increase: {} -> {}"
.format(saved_mean_reward, mean_5ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_5ep_reward
updates_queue.put(UpdateMessage(UPDATE_STATUS_FINISH, None, None))
def learn(env,
network,
seed=None,
pool=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_initial_eps=1.0,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=1,
checkpoint_freq=100,
learning_starts=1000,
gamma=1.0,
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,
callback=None,
experiment_name='unnamed',
load_path=None,
**network_kwargs):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
set to None to disable printing
batch_size: int
size of a batched sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
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.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
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 total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
experiment_name: str
name of the experiment (default: trial)
load_path: str
path to load the model from. (default: None)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size,
alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
total_timesteps),
initial_p=exploration_initial_eps,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
reward_shaper = ActionAdviceRewardShaper('../completed-observations')
reward_shaper.load()
full_exp_name = '{}-{}'.format(date.today().isoformat(), experiment_name)
experiment_dir = os.path.join('experiments', full_exp_name)
if not os.path.exists(experiment_dir):
os.makedirs(experiment_dir)
summary_dir = os.path.join(experiment_dir, 'summaries')
os.makedirs(summary_dir, exist_ok=True)
summary_writer = tf.summary.FileWriter(summary_dir)
checkpoint_dir = os.path.join(experiment_dir, 'checkpoints')
os.makedirs(checkpoint_dir, exist_ok=True)
with tempfile.TemporaryDirectory() as td:
td = checkpoint_dir or td
os.makedirs(td, exist_ok=True)
model_file = os.path.join(td, "best_model")
model_saved = False
saved_mean_reward = None
if os.path.exists(model_file):
print('Model is loading')
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
episode_rewards = []
update_step_t = 0
while update_step_t < total_timesteps:
# Reset the environment
obs = env.reset()
obs = StatePreprocessor.process(obs)
episode_rewards.append(0.0)
reset = True
done = False
# Sample the episode until it is completed
act_step_t = update_step_t
while not done:
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(act_step_t)
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(act_step_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. - exploration.value(act_step_t) +
exploration.value(act_step_t) /
float(env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
biases = reward_shaper.get_action_potentials(obs)
action = act(np.array(obs)[None],
biases,
update_eps=update_eps,
**kwargs)[0]
reset = False
pairs = env.step(action)
action, (new_obs, rew, done, _) = pairs[-1]
# Write down the real reward but learn from normalized version
episode_rewards[-1] += rew
rew = np.sign(rew) * np.log(1 + np.abs(rew))
new_obs = StatePreprocessor.process(new_obs)
logger.log('{}/{} obs {} action {}'.format(
act_step_t, total_timesteps, obs, action))
act_step_t += 1
if len(new_obs) == 0:
done = True
else:
replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
# Post episode logging
summary = tf.Summary(value=[
tf.Summary.Value(tag="rewards",
simple_value=episode_rewards[-1])
])
summary_writer.add_summary(summary, act_step_t)
summary = tf.Summary(
value=[tf.Summary.Value(tag="eps", simple_value=update_eps)])
summary_writer.add_summary(summary, act_step_t)
summary = tf.Summary(value=[
tf.Summary.Value(tag="episode_steps",
simple_value=act_step_t - update_step_t)
])
summary_writer.add_summary(summary, act_step_t)
mean_5ep_reward = round(np.mean(episode_rewards[-5:]), 1)
num_episodes = len(episode_rewards)
if print_freq is not None and num_episodes % print_freq == 0:
logger.record_tabular("steps", act_step_t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 5 episode reward", mean_5ep_reward)
logger.record_tabular("% time spent exploring",
int(100 * exploration.value(act_step_t)))
logger.dump_tabular()
# Do the learning
start = time.time()
while update_step_t < min(act_step_t, total_timesteps):
if update_step_t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(
batch_size,
beta=beta_schedule.value(update_step_t))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(
batch_size)
weights, batch_idxes = np.ones_like(rewards), None
biases_t = pool.map(reward_shaper.get_action_potentials,
obses_t)
biases_tp1 = pool.map(reward_shaper.get_action_potentials,
obses_tp1)
td_errors, weighted_error = train(obses_t, biases_t,
actions, rewards,
obses_tp1, biases_tp1,
dones, weights)
# Loss logging
summary = tf.Summary(value=[
tf.Summary.Value(tag='weighted_error',
simple_value=weighted_error)
])
summary_writer.add_summary(summary, update_step_t)
if prioritized_replay:
new_priorities = np.abs(
td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(
batch_idxes, new_priorities)
if update_step_t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
update_step_t += 1
stop = time.time()
logger.log("Learning took {:.2f} seconds".format(stop - start))
if checkpoint_freq is not None and num_episodes % checkpoint_freq == 0:
# Periodically save the model and the replay buffer
rec_model_file = os.path.join(
td, "model_{}_{:.2f}".format(num_episodes,
mean_5ep_reward))
save_variables(rec_model_file)
buffer_file = os.path.join(
td, "buffer_{}_{}".format(num_episodes, update_step_t))
with open(buffer_file, 'wb') as foutput:
cloudpickle.dump(replay_buffer, foutput)
# Check whether it is best
if saved_mean_reward is None or mean_5ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log(
"Saving model due to mean reward increase: {} -> {}"
.format(saved_mean_reward, mean_5ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_5ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(
saved_mean_reward))
load_variables(model_file)
return act
def save(self, save_path):
if save_path is not None:
info('saving vars to ' + save_path)
U.save_variables(save_path)
else:
info('save_path is None, not saving')
def save(self, path):
save_variables(path)
def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
initial_exploration_p=1.0,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
target_network_update_freq=100,
prioritized_replay=True,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
callback=None,
load_path=None,
pretraining_obs=None,
pretraining_targets=None,
pretrain_steps=1000,
pretrain_experience=None,
pretrain_num_episodes=0,
double_q=True,
expert_qfunc=None,
aggrevate_steps=0,
pretrain_lr=1e-4,
sampling_starts=0,
beb_agent=None,
qvalue_file="qvalue.csv",
**network_kwargs):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
set to None to disable printing
batch_size: int
size of a batched sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
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.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
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 total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
beb_agent: takes Q values and suggests actions after adding beb bonus
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
nenvs = env.num_envs
print("Bayes-DeepQ:", env.num_envs)
print("Total timesteps", total_timesteps)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, train_target, copy_target_to_q, debug = brl_deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
pretrain_optimizer=tf.train.AdamOptimizer(learning_rate=pretrain_lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise,
double_q=double_q)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size,
alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
total_timesteps),
initial_p=initial_exploration_p,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
print("Model will be saved at ", model_file)
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
print('Loaded model from {}'.format(load_path))
if pretraining_obs is not None:
# pretrain target and copy to qfunc
print("Pretrain steps ", pretrain_steps)
for i in range(pretrain_steps):
pretrain_errors = train_target(pretraining_obs,
pretraining_targets)
if i % 500 == 0:
print("Step {}".format(i), np.mean(pretrain_errors))
if np.mean(pretrain_errors) < 1e-5:
break
min_rew = 0
# copy all pre-experiences
if pretrain_experience is not None:
for obs, action, rew, new_obs, done in zip(*pretrain_experience):
replay_buffer.add(obs, action, rew, new_obs, float(done))
print("Added {} samples to ReplayBuffer".format(
len(replay_buffer._storage)))
min_rew = min(rew, min_rew)
print("Pretrain Error", np.mean(pretrain_errors))
else:
print("Skipping pretraining")
update_target()
print("Save the pretrained model", model_file)
save_variables(model_file)
episode_reward = np.zeros(nenvs, dtype=np.float32)
saved_mean_reward = None
obs = env.reset()
reset = True
epoch_episode_rewards = []
epoch_episode_steps = []
epoch_actions = []
epoch_episodes = 0
episode_rewards_history = deque(maxlen=100)
episode_step = np.zeros(nenvs, dtype=int)
episodes = 0 #scalar
start = 0
if expert_qfunc is None:
aggrevate_steps = 0
# if pretraining_obs is None or pretraining_obs.size == 0:
# episode_rewards = []
# else:
# episode_rewards = [[0.0]] * pretrain_num_episodes
# start = len(pretraining_obs)
# if print_freq is not None:
# for t in range(0, len(pretraining_obs), print_freq):
# logger.record_tabular("steps", t)
# logger.record_tabular("episodes", pretrain_num_episodes)
# logger.record_tabular("mean 100 episode reward", min_rew)
# logger.record_tabular("% time spent exploring", 0)
# logger.dump_tabular()
# print("pretraining episodes", pretrain_num_episodes, "steps {}/{}".format(t, total_timesteps))
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
print("Aggrevate: Model will be saved at ", model_file)
model_saved = False
for i in range(aggrevate_steps):
obses_t, values = [], []
for j in range(30):
# TODO: 30 should be changed to max horizon?
t = np.random.randint(50) + 1
obs = env.reset()
for k in range(t):
action, value = act(np.array(obs)[None],
update_eps=exploration.value(i))
obs, rew, done, _ = env.step(action)
obses_t.extend(obs)
# Roll out expert policy
episode_reward[:] = 0
dones = np.array([False] * obs.shape[0])
for k in range(51 - t):
obs, rew, done, _ = env.step(
[expert_qfunc.step(o) for o in obs])
dones[done] = True
rew[dones] = 0
episode_reward += 0.95**k * rew
# TODO: change this to exploration-savvy action
# action = np.random.randint(env.action_space.n, size=len(obs))
# Rocksample specific, take sensing actions
# prob = np.array([1] * 6 + [2] * (env.action_space.n - 6), dtype=np.float32)
# prob = prob / np.sum(prob)
# action = np.random.choice(env.action_space.n, p=prob, size=len(action))
# new_obs, rew, done, _ = env.step(action)
# value = rew.copy()
# value[np.logical_not(done)] += gamma * np.max(expert_qfunc.value(new_obs[np.logical_not(done)]), axis=1)
# current_value[tuple(np.array([np.arange(len(action)), action]))] = value
# episode reward
# episode_reward[np.logical_not(done)] += np.max(current_value[np.logical_not(done)], axis=1)
# episode_rewards_history.extend(np.max(current_value, axis=1))
value[tuple([np.arange(len(action)), action])] = episode_reward
values.extend(value)
print("Aggrevate got {} / {} new data".format(
obs.shape[0] * 30, len(obses_t)))
# print("Mean expected cost at the explored points", np.mean(np.max(values, axis=1)))
for j in range(1000):
obs, val = np.array(obses_t), np.array(values)
# indices = np.random.choice(len(obs), min(1000, len(obses_t)))
aggrevate_errors = train_target(obs, val)
if np.mean(aggrevate_errors) < 1e-5:
print("Aggrevate Step {}, {}".format(i, j),
np.mean(aggrevate_errors))
break
print("Aggrevate Step {}, {}".format(i, j),
np.mean(aggrevate_errors))
update_target()
print("Save the aggrevate model", i, model_file)
# Evaluate current policy
episode_reward[:] = 0
obs = env.reset()
num_episodes = 0
k = np.zeros(len(obs))
while num_episodes < 100:
action, _ = act(np.array(obs)[None], update_eps=0.0)
# print(action)
obs, rew, done, _ = env.step(action)
episode_reward += 0.95**k * rew
k += 1
for d in range(len(done)):
if done[d]:
episode_rewards_history.append(episode_reward[d])
episode_reward[d] = 0.
k[d] = 0
num_episodes += 1
mean_1000ep_reward = round(np.mean(episode_rewards_history), 2)
print("Mean discounted reward", mean_1000ep_reward)
logger.record_tabular("mean 100 episode reward",
mean_1000ep_reward)
logger.dump_tabular()
save_variables(model_file)
t = 0 # could start from pretrain-steps
epoch = 0
while True:
epoch += 1
if t >= total_timesteps:
break
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
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. - exploration.value(
t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
# no randomization
# update_eps = 0
print('update_eps', int(100 * exploration.value(t)))
qv_error = []
obs = env.reset()
for m in range(100):
action, q_values = act(np.array(obs)[None],
update_eps=update_eps,
**kwargs)
if beb_agent is not None:
action = beb_agent.step(obs, action, q_values,
exploration.value(t))
# if expert_qfunc is not None:
# v = expert_qfunc.value(obs)
# qv_error += [v - q_values[0]]
env_action = action
reset = False
new_obs, rew, done, info = env.step(env_action)
if t >= sampling_starts:
# Store transition in the replay buffer.
replay_buffer.add(obs, action, rew, new_obs, done)
obs = new_obs
episode_reward += rew
episode_step += 1
for d in range(len(done)):
if done[d]:
# Episode done.
# discount(np.array(rewards), gamma) consider doing discount
epoch_episode_rewards.append(episode_reward[d])
episode_rewards_history.append(episode_reward[d])
epoch_episode_steps.append(episode_step[d])
episode_reward[d] = 0.
episode_step[d] = 0
epoch_episodes += 1
episodes += 1
t += 100 * nenvs
if t > learning_starts:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(
batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(
batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, actions, rewards, obses_tp1, dones,
weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes,
new_priorities)
if target_network_update_freq is not None and t > sampling_starts \
and epoch % target_network_update_freq == 0:
# Update target network periodically.
print("Update target")
update_target()
mean_1000ep_reward = round(np.mean(episode_rewards_history), 2)
num_episodes = episodes
if print_freq is not None:
logger.record_tabular("steps", t)
logger.record_tabular("td errors", np.mean(td_errors))
logger.record_tabular("td errors std",
np.std(np.abs(td_errors)))
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 1000 episode reward",
mean_1000ep_reward)
logger.record_tabular("% time spent exploring",
int(100 * exploration.value(t)))
logger.dump_tabular()
print("episodes", num_episodes,
"steps {}/{}".format(t, total_timesteps))
if (checkpoint_freq is not None and t > learning_starts
and len(episode_rewards_history) >= 1000):
if saved_mean_reward is None or mean_1000ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log(
"Saving model due to mean reward increase: {} -> {}"
.format(saved_mean_reward, mean_1000ep_reward))
print("saving model")
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_1000ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(
saved_mean_reward))
load_variables(model_file)
return act
def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.5,
initial_exploration_p=1.0,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=1,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1,
gamma=1.0,
target_network_update_freq=40000,#10000,
prioritized_replay=True,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
callback=None,
load_path=None,
double_q=True,
obs_dim=None,
qmdp_expert=None,
**network_kwargs
):
"""Train a bootstrap-dqn model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
set to None to disable printing
batch_size: int
size of a batched sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
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.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
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 total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
qmdp_expert: takes obs, bel -> returns qmdp q-vals
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
nenvs = env.num_envs
print("Bootstrap DQN with {} envs".format(nenvs))
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
# import IPython; IPython.embed()
#assert isinstance(env.envs[0].env.env.env, ExplicitBayesEnv)
#belief_space = env.envs[0].env.env.env.belief_space
#observation_space = env.envs[0].env.env.env.internal_observation_space
obs_space = env.observation_space
assert obs_dim is not None
observation_space = Box(obs_space.low[:obs_dim], obs_space.high[:obs_dim], dtype=np.float32)
belief_space = Box(obs_space.low[obs_dim:], obs_space.high[obs_dim:], dtype=np.float32)
num_experts = belief_space.high.size
# print("Num experts", num_experts)
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
def make_bel_ph(name):
return ObservationInput(belief_space, name=name)
q_func = build_q_func(network, num_experts, **network_kwargs)
print('=============== got qfunc ============== ')
act, train, update_target, debug = residual_bqn_fixed_expert.build_train(
make_obs_ph=make_obs_ph,
make_bel_ph=make_bel_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise,
double_q=double_q
)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction * total_timesteps),
initial_p=initial_exploration_p,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_reward = np.zeros(nenvs, dtype = np.float32)
saved_mean_reward = None
reset = True
epoch_episode_rewards = []
epoch_episode_steps = []
epoch_actions = []
epoch_episodes = 0
episode_rewards_history = deque(maxlen=1000)
episode_step = np.zeros(nenvs, dtype = int)
episodes = 0 #scalar
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
print("Model will be saved at " , model_file)
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
print('Loaded model from {}'.format(load_path))
t = 0
while t < total_timesteps:
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
update_eps = exploration.value(t)
update_param_noise_threshold = 0.
obs = env.reset()
episode_reward = np.zeros(nenvs, dtype = np.float32)
episode_step[:] = 0
obs, bel = obs[:, :-belief_space.shape[0]], obs[:, -belief_space.shape[0]:]
expert_qval = qmdp_expert(obs, bel)
start_time = timer.time()
horizon = 100
for m in range(horizon):
action, q_values = act(np.array(obs)[None], np.array(bel)[None], np.array(expert_qval)[None], update_eps=update_eps, **kwargs)
env_action = action
new_obs, rew, done, info = env.step(env_action)
new_obs, new_bel = new_obs[:, :-belief_space.shape[0]], new_obs[:, -belief_space.shape[0]:]
new_expert_qval = qmdp_expert(new_obs, new_bel)
# Store transition in the replay buffer.
replay_buffer.add(obs, bel, expert_qval, action, rew, new_obs, new_bel, new_expert_qval, done)
# if np.random.rand() < 0.05:
# # # write to file
# # with open('rbqn_fixed_expert.csv', 'a') as f:
# # out = ','.join(str(np.around(x,2)) for x in [bel[0], obs[0], q_values[0]])
# # f.write(out + "\n")
# print(np.around(bel[-1], 2), rew[-1], np.around(q_values[-1], 1), np.around(expert_qval[-1], 1))
obs = new_obs
bel = new_bel
expert_qval = new_expert_qval
episode_reward += 0.95 ** episode_step * rew
episode_step += 1
# print(action, done, obs)
for d in range(len(done)):
if done[d]:
epoch_episode_rewards.append(episode_reward[d])
episode_rewards_history.append(episode_reward[d])
epoch_episode_steps.append(episode_step[d])
episode_reward[d] = 0.
episode_step[d] = 0
epoch_episodes += 1
episodes += 1
# import IPython;IPython.embed(); #import sys; sys.exit(0)
print("Took {}".format(timer.time() - start_time))
t += horizon * nenvs
num_experts = 16
if t > learning_starts and t % train_freq == 0:
for _ in range(num_experts):
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(batch_size, beta=beta_schedule.value(t))
if experience is None:
continue
obses_t, bels_t, expert_qvals, actions, rewards, obses_tp1, bels_tp1, expert_qvals_1, dones, weights, batch_idxes = experience
else:
experience = replay_buffer.sample(batch_size)
if experience is None:
continue
obses_t, bels_t, expert_qvals, actions, rewards, obses_tp1, bels_tp1, expert_qvals_1, dones = experience
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, bels_t, expert_qvals, actions, rewards, obses_tp1, bels_tp1, expert_qvals_1, dones, weights)
if np.random.rand() < 0.01:
print("TD error", np.around(td_errors, 1))
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes, new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
print("Update target")
update_target()
mean_100ep_reward = round(np.mean(episode_rewards_history), 2)
num_episodes = episodes
if print_freq is not None and num_episodes % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 1000 episode reward", mean_100ep_reward)
logger.record_tabular("% time spent exploring", int(100 * exploration.value(t)))
logger.dump_tabular()
print("episodes ", num_episodes, "steps {}/{}".format(t, total_timesteps))
print("mean reward", mean_100ep_reward)
print("exploration", int(100 * exploration.value(t)))
if (checkpoint_freq is not None and t > learning_starts and
num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log("Saving model due to mean reward increase: {} -> {}".format(
saved_mean_reward, mean_100ep_reward))
print("saving model")
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(saved_mean_reward))
load_variables(model_file)
return act
def save_policy(self, name):
U.save_variables(name, variables=self.get_variables())
def learn(env,
policy_func,
dataset,
optim_batch_size=100,
max_iters=1e4,
adam_epsilon=1e-5,
optim_stepsize=1e-3,
ckpt_dir=None,
log_dir=None,
task_name=None,
verbose=False):
val_per_iter = int(max_iters / 10)
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_func("pi", ob_space,
ac_space) # Construct network for new polic
dof = 5
# placeholder
ob_config = U.get_placeholder_cached(name="ob")
ob_target = U.get_placeholder_cached(name="goal")
obs_pos = U.get_placeholder_cached(name="obs_pos")
obs_ori = U.get_placeholder_cached(name="obs_ori")
ac = pi.pdtype.sample_placeholder([None])
stochastic = U.get_placeholder_cached(name="stochastic")
loss = tf.reduce_mean(tf.square(ac - pi.ac))
#loss = tf.reduce_mean(pi.pd.neglogp(ac))
#var_list = pi.get_trainable_variables()
all_var_list = pi.get_variables()
var_list = [
v for v in all_var_list if v.name.startswith("pi/pol")
or v.name.startswith("pi/logstd") or v.name.startswith("pi/obs")
]
adam = MpiAdam(var_list, epsilon=adam_epsilon)
lossandgrad = U.function(
[ob_config, ob_target, obs_pos, obs_ori, ac, stochastic],
[loss] + [U.flatgrad(loss, var_list)])
U.initialize()
if ckpt_dir is None:
savedir_fname = tempfile.TemporaryDirectory().name
else:
savedir_fname = osp.join(ckpt_dir, 'model')
if osp.exists(savedir_fname):
try:
U.load_variables(savedir_fname, pi.get_variables())
except:
print(
"size of the pretrained model does not match the current model"
)
adam.sync()
logger.log("Pretraining with Behavior Cloning...")
thresh = 0.1
for iter_so_far in tqdm(range(int(max_iters))):
ob_expert, ac_expert = dataset.get_next_batch(optim_batch_size,
'train')
tar = ob_expert[:, dof:2 * dof]
cur = ob_expert[:, :dof]
avo = np.zeros_like(cur)
for i in range(len(avo)):
avo[i] = ac_expert[i] - thresh * (
tar[i] - cur[i]) / np.linalg.norm(tar[i] - cur[i])
# avo = ac_expert - thresh * (tar - cur) / np.linalg.norm(tar - cur)
train_loss, g = lossandgrad(cur, tar, ob_expert[:, -6:-3],
ob_expert[:, -3:], avo, True)
adam.update(g, optim_stepsize)
if verbose and iter_so_far % val_per_iter == 0:
ob_expert, ac_expert = dataset.get_next_batch(-1, 'val')
tar = ob_expert[:, dof:2 * dof]
cur = ob_expert[:, :dof]
avo = np.zeros_like(cur)
for i in range(len(avo)):
avo[i] = ac_expert[i] - thresh * (
tar[i] - cur[i]) / np.linalg.norm(tar[i] - cur[i])
val_loss, _ = lossandgrad(cur, tar, ob_expert[:, -6:-3],
ob_expert[:, -3:], avo, True)
logger.log("Training loss: {}, Validation loss: {}".format(
np.rad2deg(np.sqrt(train_loss)),
np.rad2deg(np.sqrt(val_loss))))
U.save_variables(savedir_fname, variables=pi.get_variables())
return savedir_fname
def learn(env,
policy_func,
dataset,
optim_batch_size=256,
max_iters=5e3,
adam_epsilon=1e-7,
optim_stepsize=1e-4,
ckpt_dir=None,
log_dir=None,
task_name=None,
verbose=False):
val_per_iter = int(max_iters / 10)
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_func("pi", ob_space,
ac_space) # Construct network for new policy
dof = 5
# placeholder
ob_config = U.get_placeholder_cached(name="ob")
ob_target = U.get_placeholder_cached(name="goal")
obs_pos = U.get_placeholder_cached(name="obs_pos")
obs_ori = U.get_placeholder_cached(name="obs_ori")
ac = pi.pdtype.sample_placeholder([None])
stochastic = U.get_placeholder_cached(name="stochastic")
loss = tf.reduce_mean(tf.square(ac - pi.ac))
# loss = tf.reduce_mean(pi.pd.neglogp(ac))
all_var_list = pi.get_trainable_variables()
var_list = [
v for v in all_var_list if v.name.startswith("pi/pol")
or v.name.startswith("pi/logstd") or v.name.startswith("pi/obs")
]
AdamOp = tf.train.AdamOptimizer(learning_rate=optim_stepsize,
epsilon=adam_epsilon).minimize(
loss, var_list=var_list)
U.initialize()
if ckpt_dir is None:
savedir_fname = tempfile.TemporaryDirectory().name
else:
savedir_fname = osp.join(ckpt_dir, 'model')
if osp.exists(savedir_fname):
try:
U.load_variables(savedir_fname, pi.get_variables())
except:
print(
"size of the pretrained model does not match the current model"
)
logger.log("Pretraining with Behavior Cloning...")
thresh = 0.1
for iter_so_far in tqdm(range(int(max_iters))):
ob_expert, ac_expert = dataset.get_next_batch(optim_batch_size,
'train')
tar = ob_expert[:, dof:2 * dof]
cur = ob_expert[:, :dof]
avo = np.zeros_like(cur)
for i in range(len(avo)):
avo[i] = ac_expert[i] - thresh * (
tar[i] - cur[i]) / np.linalg.norm(tar[i] - cur[i])
# avo = ac_expert - thresh * (tar - cur) / np.linalg.norm(tar - cur)
U.get_session().run(AdamOp,
feed_dict={
ob_config: cur,
ob_target: tar,
obs_pos: ob_expert[:, -6:-3],
obs_ori: ob_expert[:, -3:],
ac: avo,
stochastic: True
})
if verbose and iter_so_far % val_per_iter == 0:
ob_expert, ac_expert = dataset.get_next_batch(-1, 'val')
tar = ob_expert[:, dof:2 * dof]
cur = ob_expert[:, :dof]
avo = np.zeros_like(cur)
for i in range(len(avo)):
avo[i] = ac_expert[i] - thresh * (
tar[i] - cur[i]) / np.linalg.norm(tar[i] - cur[i])
val_loss = U.get_session().run(loss,
feed_dict={
ob_config: cur,
ob_target: tar,
obs_pos: ob_expert[:, -6:-3],
obs_ori: ob_expert[:, -3:],
ac: avo,
stochastic: True
})
logger.log("Validation loss: {}".format(
np.rad2deg(np.sqrt(val_loss))))
allvar = pi.get_variables()
savevar = [v for v in allvar if "Adam" not in v.name]
U.save_variables(savedir_fname, variables=savevar)
return savedir_fname
def save(self, save_path):
U.save_variables(save_path, None, self.sess)
def run_main(opts):
if os.path.exists(opts.model_dir):
print('Path already exists. Remove? y for yes')
input_char = getch.getch()
if not input_char == 'y':
print('Exiting')
return
shutil.rmtree(opts.model_dir)
os.makedirs(opts.model_dir)
os.makedirs(os.path.join(opts.model_dir, 'logs'))
os.makedirs(os.path.join(opts.model_dir, 'weights'))
# Create the environment with specified arguments
state_data, action_data = process_data(opts.bc_data)
#env = gym.make('MountainCar-v0')
env = gym.make('LunarLander-v2')
env._max_episode_steps = 1200
x, model, logits = create_model()
train, loss, labels = create_training(logits)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# Create summaries
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(os.path.join(opts.model_dir, 'logs'),
sess.graph)
print(tf.global_variables())
sess.run(tf.global_variables_initializer())
update = 0
save_freq = 1000
while True:
for _ in range(25):
# Get a random batch from the data
batch_index = np.random.choice(len(state_data), 64) #Batch size
state_batch, action_batch = state_data[batch_index], action_data[
batch_index]
# Train the model.
_, cur_loss, cur_summaries = sess.run([train, loss, merged],
feed_dict={
x: state_data,
labels: action_data
})
print("Loss: {}".format(cur_loss))
train_writer.add_summary(cur_summaries, update)
update += 1
if update % save_freq == 0:
save_variables(os.path.join(opts.model_dir, 'weights',
opts.model_weights),
sess=sess)
done = False
obs = env.reset()
rewards = 0
action_freq = [0 for _ in range(4)]
while not done:
env.render()
# Handle the toggling of different application states
action = sess.run(model, feed_dict={x: [obs.flatten()]})[0]
action_freq[action] += 1
obs, reward, done, info = env.step(action)
rewards += reward
reward_summary = tf.Summary(
value=[tf.Summary.Value(tag='reward', simple_value=rewards)])
act_summary = tf.Summary(value=[
tf.Summary.Value(tag='act_distribution',
histo=log_histogram(action_freq, 1, bins=4))
])
train_writer.add_summary(reward_summary, update // 25)
train_writer.add_summary(act_summary, update // 25)
print("Num updates: {}".format(update))
print("Total reward: {}".format(rewards))
print("Action dict: {}".format(action_freq))
def save(self, path):
U.save_variables(path, sess=self.sess)
def train_dqn(opts,
seed=None,
lr=1e-3,
total_timesteps=500000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
checkpoint_freq=500000,
learning_starts=1000,
gamma=1.000,
target_network_update_freq=3000,
load_path=None):
"""
Runs the main recorder by binding certain discrete actions to keys.
"""
if os.path.exists(opts.model_dir):
print('Path already exists. Remove? y for yes')
input_char = getch.getch()
if not input_char == 'y':
print('Exiting')
return
shutil.rmtree(opts.model_dir)
os.makedirs(opts.model_dir)
os.makedirs(os.path.join(opts.model_dir, 'logs'))
os.makedirs(os.path.join(opts.model_dir, 'weights'))
#env = gym.make('MountainCar-v0')
env = gym.make('LunarLander-v2')
env._max_episode_steps = 1200
sess = get_session()
set_global_seeds(seed)
train_writer = tf.summary.FileWriter(os.path.join(opts.model_dir, 'logs'),
sess.graph)
q_func = build_q_func('mlp')
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10)
replay_buffer = ReplayBuffer(buffer_size)
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
obs = env.reset()
for t in range(total_timesteps):
# Take action and update exploration to the newest value
env.render()
update_eps = exploration.value(t)
action = act(np.array(obs)[None], update_eps=update_eps)[0]
new_obs, rew, done, _ = env.step(action)
# Store transition in the replay buffer.
replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
episode_rewards[-1] += rew
if done:
print("Exploration value: {}".format(exploration.value(t)))
print("Last 25 episode rewards: {}".format(episode_rewards[-25:]))
reward_summary = tf.Summary(value=[
tf.Summary.Value(tag='reward',
simple_value=episode_rewards[-1])
])
train_writer.add_summary(reward_summary, t)
obs = env.reset()
episode_rewards.append(0.0)
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(
batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors, summary = train(obses_t, actions, rewards, obses_tp1,
dones, weights)
train_writer.add_summary(summary, t)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
if t > learning_starts and t % checkpoint_freq == 0:
save_variables(
os.path.join(opts.model_dir, 'weights', '{}.model'.format(t)))
save_variables(os.path.join(opts.model_dir, 'weights', 'last.model'))
def save_variables(self, save_path):
tf_util.save_variables(save_path, sess=self.sess)
def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
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,
debug_flag=False,
dpsr_replay=False,
dpsr_replay_alpha1=0.6,
dpsr_replay_alpha2=0.6,
dpsr_replay_candidates_size=5,
dpsr_common_replacement_candidates_number=128,
dpsr_replay_beta_iters=None,
dpsr_replay_beta0=0.4,
dpsr_replay_eps=1e-6,
dpsr_state_recycle_max_priority_set=True,
state_recycle_freq=500,
param_noise=False,
callback=None,
load_path=None,
atari_env=True,
**network_kwargs):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
batch_size: int
size of a batch sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
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.
checkpoint_path: str
the saving path of the checkpoint files
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
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 total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
debug_flag: bool
if True DEBUG mode will be switched on
dpsr_replay: bool
if True DPSR replay buffer will be used
dpsr_replay_alpha1: float
alpha1 parameter for DPSR replay buffer
dpsr_replay_alpha2: float
alpha2 parameter for DPSR replay buffer
dpsr_replay_candidates_size: int
candidates size parameter for DPSR replay buffer state recycle
dpsr_common_replacement_candidates_number: int
candidates size parameter for DPSR replay buffer common replacement
dpsr_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 total_timesteps.
dpsr_replay_beta0: float
initial value of beta for prioritized replay buffer
dpsr_replay_eps: float
epsilon to add to the TD errors when updating priorities.
dpsr_state_recycle_max_priority_set: bool
if True priority will be set as MAX when doing state recycling
state_recycle_freq: int
do state recycling every 'state_recycle_freq' steps
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
atari_env: bool
if True the env is an atari env
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size,
alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
elif dpsr_replay:
replay_buffer = DoublePrioritizedStateRecycledReplayBuffer(
buffer_size,
alpha1=dpsr_replay_alpha1,
alpha2=dpsr_replay_alpha2,
candidates_size=dpsr_replay_candidates_size,
# Not Used: env_id=env.env.spec.id
env_id=None)
if dpsr_replay_beta_iters is None:
dpsr_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(dpsr_replay_beta_iters,
initial_p=dpsr_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
reset = True
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
for t in range(total_timesteps):
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
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. - exploration.value(
t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
action = act(np.array(obs)[None], update_eps=update_eps,
**kwargs)[0]
env_action = action
reset = False
env_clone_state = None
if dpsr_replay:
env_clone_state = env.clone_state() if atari_env \
else copy.deepcopy(env.envs[0].env)
new_obs, rew, done, _ = env.step(env_action)
# Store transition in the replay buffer.
if dpsr_replay:
if replay_buffer.not_full():
replay_buffer.add(obs, action, rew, new_obs, float(done),
env_clone_state, t)
elif state_recycle_freq and t % state_recycle_freq == 0:
current_env_copy = None
if not atari_env:
current_env_copy = copy.deepcopy(env.envs[0].env)
candidates_idxes, candidates = replay_buffer.replacement_candidates(
)
candidates_recycled = []
for candidate in candidates:
cand_obs, cand_old_act, *_, cand_state, cand_t = candidate
if atari_env:
new_env = copy.deepcopy(env)
new_env.reset()
new_env.restore_state(cand_state)
else:
env.envs[0].env = cand_state
new_action_cand = act(np.array(cand_obs)[None],
update_eps=0.0,
**kwargs)[0]
# make sure that a new experience is made
if new_action_cand != cand_old_act:
new_action = new_action_cand
else:
while True:
new_action_cand = env.action_space.sample()
if new_action_cand != cand_old_act:
new_action = new_action_cand
break
if atari_env:
new_new_obs, new_rew, new_done, _ = new_env.step(
new_action)
else:
new_new_obs, new_rew, new_done, _ = env.step(
new_action)
new_data = (cand_obs, new_action, new_rew, new_new_obs,
new_done, cand_state, t)
candidates_recycled.append(new_data)
# get the new TDEs after recycling
cand_obses = np.array(
[data[0] for data in candidates_recycled])
cand_acts = np.array(
[data[1] for data in candidates_recycled])
cand_rews = np.array(
[data[2] for data in candidates_recycled])
cand_new_obses = np.array(
[data[3] for data in candidates_recycled])
cand_dones = np.array(
[data[4] for data in candidates_recycled])
cand_weights = np.zeros_like(cand_rews)
cand_td_errors = train(cand_obses, cand_acts, cand_rews,
cand_new_obses, cand_dones,
cand_weights)
new_cand_priorities = np.abs(
cand_td_errors) + dpsr_replay_eps
replay_buffer.update_priorities(candidates_idxes,
new_cand_priorities)
replay_buffer.state_recycle(
candidates_idxes, candidates_recycled, cand_td_errors,
dpsr_state_recycle_max_priority_set)
replay_buffer.add(obs, action, rew, new_obs, float(done),
env_clone_state, t)
if not atari_env:
env.envs[0].env = current_env_copy
else:
# common_replacement_candidates_number = 128
candidates_idxes, candidates = replay_buffer.replacement_candidates(
dpsr_common_replacement_candidates_number)
cand_timestamps = [
candidate[-1] for candidate in candidates
]
replace_idx = candidates_idxes[np.argmin(cand_timestamps)]
replay_buffer.add(obs, action, rew, new_obs, float(done),
env_clone_state, t, replace_idx)
else:
replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
episode_rewards[-1] += rew
if done:
obs = env.reset()
episode_rewards.append(0.0)
reset = True
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(
batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
elif dpsr_replay:
experience = replay_buffer.sample(
batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, env_states,
timestamps, weights, batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(
batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, actions, rewards, obses_tp1, dones,
weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes,
new_priorities)
elif dpsr_replay:
new_priorities = np.abs(td_errors) + dpsr_replay_eps
replay_buffer.update_priorities(batch_idxes,
new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
mean_100ep_reward = np.round(np.mean(episode_rewards[-101:-1]), 1)
mean_10ep_reward = np.round(np.mean(episode_rewards[-11:-1]), 1)
mean_5ep_reward = np.round(np.mean(episode_rewards[-6:-1]), 1)
last_1ep_reward = np.round(np.mean(episode_rewards[-2:-1]), 1)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(
episode_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward)
logger.record_tabular("mean 10 episode reward",
mean_10ep_reward)
logger.record_tabular("mean 5 episode reward", mean_5ep_reward)
logger.record_tabular("last episode reward", last_1ep_reward)
logger.record_tabular("% time spent exploring",
int(100 * exploration.value(t)))
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts
and num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log(
"Saving model due to mean reward increase: {} -> {}"
.format(saved_mean_reward, mean_100ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(
saved_mean_reward))
load_variables(model_file)
return act
obs = np.array(
[[np.cos(row.obnow1),
np.sin(row.obnow1), row.obnow2]])
action = np.array([[row.action]])
r = np.array([[row.reward]])
new_obs = np.array([[row.obnext1, row.obnext2, row.obnext3]])
agent.store_transition(obs, action, r, new_obs, np.zeros_like(r))
# training
for t_train in range(t_train_time):
cl, al = agent.train()
epoch_critic_losses.append(cl)
epoch_actor_losses.append(al)
print(
'step' + str(t_train) + ',critic_loss:' + str(cl) +
',action_loss:', str(al))
save_variables('ddpg_model')
else:
load_variables(load_path)
# plt.figure(1)
# plt.plot(epoch_critic_losses)
# plt.plot(epoch_actor_losses)
# plt.show()
env = gym.make('Pendulum-v0')
obs = env.reset()
for time in range(t_test_time):
action, q, _, _ = agent.step(obs, apply_noise=False, compute_Q=True)
s_, r, done, _ = env.step(action)
obs = s_
env.render()
def learn(env,
use_ddpg=False,
gamma=0.9,
use_rs=False,
controller_kargs={},
option_kargs={},
seed=None,
total_timesteps=100000,
print_freq=100,
callback=None,
checkpoint_path=None,
checkpoint_freq=10000,
load_path=None,
**others):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
use_ddpg: bool
whether to use DDPG or DQN to learn the option's policies
gamma: float
discount factor
use_rs: bool
use reward shaping
controller_kargs
arguments for learning the controller policy.
option_kargs
arguments for learning the option policies.
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
total_timesteps: int
number of env steps to optimizer for
print_freq: int
how often to print out training progress
set to None to disable printing
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.
load_path: str
path to load the model from. (default: None)
Returns
-------
act: ActWrapper (meta-controller)
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
act: ActWrapper (option policies)
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
controller = ControllerDQN(env, **controller_kargs)
if use_ddpg:
options = OptionDDPG(env, gamma, total_timesteps, **option_kargs)
else:
options = OptionDQN(env, gamma, total_timesteps, **option_kargs)
option_s = None # State where the option initiated
option_id = None # Id of the current option being executed
option_rews = [] # Rewards obtained by the current option
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
options.reset()
reset = True
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
for t in range(total_timesteps):
if callback is not None:
if callback(locals(), globals()):
break
# Selecting an option if needed
if option_id is None:
valid_options = env.get_valid_options()
option_s = obs
option_id = controller.get_action(option_s, valid_options)
option_rews = []
# Take action and update exploration to the newest value
action = options.get_action(env.get_option_observation(option_id),
t, reset)
reset = False
new_obs, rew, done, info = env.step(action)
# Saving the real reward that the option is getting
if use_rs:
option_rews.append(info["rs-reward"])
else:
option_rews.append(rew)
# Store transition for the option policies
for _s, _a, _r, _sn, _done in env.get_experience():
options.add_experience(_s, _a, _r, _sn, _done)
# Learn and update the target networks if needed for the option policies
options.learn(t)
options.update_target_network(t)
# Update the meta-controller if needed
# Note that this condition always hold if done is True
if env.did_option_terminate(option_id):
option_sn = new_obs
option_reward = sum(
[_r * gamma**_i for _i, _r in enumerate(option_rews)])
valid_options = [] if done else env.get_valid_options()
controller.add_experience(option_s, option_id, option_reward,
option_sn, done, valid_options,
gamma**(len(option_rews)))
controller.learn()
controller.update_target_network()
controller.increase_step()
option_id = None
obs = new_obs
episode_rewards[-1] += rew
if done:
obs = env.reset()
options.reset()
episode_rewards.append(0.0)
reset = True
# General stats
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(
episode_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward)
logger.dump_tabular()
if (checkpoint_freq is not None and num_episodes > 100
and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log(
"Saving model due to mean reward increase: {} -> {}"
.format(saved_mean_reward, mean_100ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(
saved_mean_reward))
#load_variables(model_file)
return controller.act, options.act
def learn(env,
network,
seed=None,
use_crm=False,
use_rs=False,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
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,
callback=None,
load_path=None,
**network_kwargs):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
use_crm: bool
use counterfactual experience to train the policy
use_rs: bool
use reward shaping
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
batch_size: int
size of a batch sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
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.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
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 total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Adjusting hyper-parameters by considering the number of RM states for crm
if use_crm:
rm_states = env.get_num_rm_states()
buffer_size = rm_states * buffer_size
batch_size = rm_states * batch_size
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size,
alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
reset = True
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
for t in range(total_timesteps):
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
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. - exploration.value(
t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
action = act(np.array(obs)[None], update_eps=update_eps,
**kwargs)[0]
env_action = action
reset = False
new_obs, rew, done, info = env.step(env_action)
# Store transition in the replay buffer.
if use_crm:
# Adding counterfactual experience (this will alrady include shaped rewards if use_rs=True)
experiences = info["crm-experience"]
elif use_rs:
# Include only the current experince but shape the reward
experiences = [info["rs-experience"]]
else:
# Include only the current experience (standard deepq)
experiences = [(obs, action, rew, new_obs, float(done))]
# Adding the experiences to the replay buffer
for _obs, _action, _r, _new_obs, _done in experiences:
replay_buffer.add(_obs, _action, _r, _new_obs, _done)
obs = new_obs
episode_rewards[-1] += rew
if done:
obs = env.reset()
episode_rewards.append(0.0)
reset = True
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(
batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(
batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, actions, rewards, obses_tp1, dones,
weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes,
new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(
episode_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward)
logger.record_tabular("% time spent exploring",
int(100 * exploration.value(t)))
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts
and num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log(
"Saving model due to mean reward increase: {} -> {}"
.format(saved_mean_reward, mean_100ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(
saved_mean_reward))
load_variables(model_file)
return act
def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
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,
callback=None,
load_path=None,
**network_kwargs
):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
batch_size: int
size of a batch sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
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.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
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 total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise
)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction * total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
############################## RL-S Prepare #############################################
# model saved name
saved_name = "0817"
#####
# Setup Training Record
#####
save_new_data = False
create_new_file = False
create_new_file_rule = create_new_file
save_new_data_rule = save_new_data
create_new_file_RL = False
save_new_data_RL = save_new_data
create_new_file_replay_buffer = False
save_new_data_replay_buffer = save_new_data
is_training = False
trajectory_buffer = deque(maxlen=20)
if create_new_file_replay_buffer:
if osp.exists("recorded_replay_buffer.txt"):
os.remove("recorded_replay_buffer.txt")
else:
replay_buffer_dataset = np.loadtxt("recorded_replay_buffer.txt")
for data in replay_buffer_dataset:
obs, action, rew, new_obs, done = _extract_data(data)
replay_buffer.add(obs, action, rew, new_obs, done)
recorded_replay_buffer_outfile = open("recorded_replay_buffer.txt","a")
recorded_replay_buffer_format = " ".join(("%f",)*31)+"\n"
#####
# Setup Rule-based Record
#####
create_new_file_rule = True
# create state database
if create_new_file_rule:
if osp.exists("state_index_rule.dat"):
os.remove("state_index_rule.dat")
os.remove("state_index_rule.idx")
if osp.exists("visited_state_rule.txt"):
os.remove("visited_state_rule.txt")
if osp.exists("visited_value_rule.txt"):
os.remove("visited_value_rule.txt")
visited_state_rule_value = []
visited_state_rule_counter = 0
else:
visited_state_rule_value = np.loadtxt("visited_value_rule.txt")
visited_state_rule_value = visited_state_rule_value.tolist()
visited_state_rule_counter = len(visited_state_rule_value)
visited_state_rule_outfile = open("visited_state_rule.txt", "a")
visited_state_format = " ".join(("%f",)*14)+"\n"
visited_value_rule_outfile = open("visited_value_rule.txt", "a")
visited_value_format = " ".join(("%f",)*2)+"\n"
visited_state_tree_prop = rindex.Property()
visited_state_tree_prop.dimension = 14
visited_state_dist = np.array([[0.2, 2, 10, 0.2, 2, 10, 0.2, 2, 10, 0.2, 2, 10, 0.2, 2]])
visited_state_rule_tree = rindex.Index('state_index_rule',properties=visited_state_tree_prop)
#####
# Setup RL-based Record
#####
if create_new_file_RL:
if osp.exists("state_index_RL.dat"):
os.remove("state_index_RL.dat")
os.remove("state_index_RL.idx")
if osp.exists("visited_state_RL.txt"):
os.remove("visited_state_RL.txt")
if osp.exists("visited_value_RL.txt"):
os.remove("visited_value_RL.txt")
if create_new_file_RL:
visited_state_RL_value = []
visited_state_RL_counter = 0
else:
visited_state_RL_value = np.loadtxt("visited_value_RL.txt")
visited_state_RL_value = visited_state_RL_value.tolist()
visited_state_RL_counter = len(visited_state_RL_value)
visited_state_RL_outfile = open("visited_state_RL.txt", "a")
visited_state_format = " ".join(("%f",)*14)+"\n"
visited_value_RL_outfile = open("visited_value_RL.txt", "a")
visited_value_format = " ".join(("%f",)*2)+"\n"
visited_state_tree_prop = rindex.Property()
visited_state_tree_prop.dimension = 14
visited_state_dist = np.array([[0.2, 2, 10, 0.2, 2, 10, 0.2, 2, 10, 0.2, 2, 10, 0.2, 2]])
visited_state_RL_tree = rindex.Index('state_index_RL',properties=visited_state_tree_prop)
############################## RL-S Prepare End #############################################
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
reset = True
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
for t in range(total_timesteps):
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
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. - exploration.value(t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs['update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
action, q_function_cz = act(np.array(obs)[None], update_eps=update_eps, **kwargs)
# RLS_action = generate_RLS_action(obs,q_function_cz,action,visited_state_rule_value,
# visited_state_rule_tree,visited_state_RL_value,
# visited_state_RL_tree,is_training)
RLS_action = 0
env_action = RLS_action
reset = False
new_obs, rew, done, _ = env.step(env_action)
########### Record data in trajectory buffer and local file, but not in replay buffer ###########
trajectory_buffer.append((obs, action, float(rew), new_obs, float(done)))
# Store transition in the replay buffer.
# replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
episode_rewards[-1] += rew # safe driving is 1, collision is 0
while len(trajectory_buffer)>10:
# if safe driving for 10(can be changed) steps, the state is regarded as safe
obs_left, action_left, rew_left, new_obs_left, done_left = trajectory_buffer.popleft()
# save this state in local replay buffer file
if save_new_data_replay_buffer:
recorded_data = _wrap_data(obs_left, action_left, rew_left, new_obs_left, done_left)
recorded_replay_buffer_outfile.write(recorded_replay_buffer_format % tuple(recorded_data))
# put this state in replay buffer
replay_buffer.add(obs_left[0], action_left, float(rew_left), new_obs_left[0], float(done_left))
action_to_record = action_left
r_to_record = rew_left
obs_to_record = obs_left
# save this state in rule-based or RL-based visited state
if action_left == 0:
if save_new_data_rule:
visited_state_rule_value.append([action_to_record,r_to_record])
visited_state_rule_tree.insert(visited_state_rule_counter,
tuple((obs_to_record-visited_state_dist).tolist()[0]+(obs_to_record+visited_state_dist).tolist()[0]))
visited_state_rule_outfile.write(visited_state_format % tuple(obs_to_record[0]))
visited_value_rule_outfile.write(visited_value_format % tuple([action_to_record,r_to_record]))
visited_state_rule_counter += 1
else:
if save_new_data_RL:
visited_state_RL_value.append([action_to_record,r_to_record])
visited_state_RL_tree.insert(visited_state_RL_counter,
tuple((obs_to_record-visited_state_dist).tolist()[0]+(obs_to_record+visited_state_dist).tolist()[0]))
visited_state_RL_outfile.write(visited_state_format % tuple(obs_to_record[0]))
visited_value_RL_outfile.write(visited_value_format % tuple([action_to_record,r_to_record]))
visited_state_RL_counter += 1
################# Record data end ########################
if done:
"""
Get collision or out of multilane map
"""
####### Record the trajectory data and add data in replay buffer #########
_, _, rew_right, _, _ = trajectory_buffer[-1]
while len(trajectory_buffer)>0:
obs_left, action_left, rew_left, new_obs_left, done_left = trajectory_buffer.popleft()
action_to_record = action_left
r_to_record = (rew_right-rew_left)*gamma**len(trajectory_buffer) + rew_left
# record in local replay buffer file
if save_new_data_replay_buffer:
obs_to_record = obs_left
recorded_data = _wrap_data(obs_left, action_left, r_to_record, new_obs_left, done_left)
recorded_replay_buffer_outfile.write(recorded_replay_buffer_format % tuple(recorded_data))
# record in replay buffer for trainning
replay_buffer.add(obs_left[0], action_left, float(r_to_record), new_obs_left[0], float(done_left))
# save visited rule/RL state data in local file
if action_left == 0:
if save_new_data_rule:
visited_state_rule_value.append([action_to_record,r_to_record])
visited_state_rule_tree.insert(visited_state_rule_counter,
tuple((obs_to_record-visited_state_dist).tolist()[0]+(obs_to_record+visited_state_dist).tolist()[0]))
visited_state_rule_outfile.write(visited_state_format % tuple(obs_to_record[0]))
visited_value_rule_outfile.write(visited_value_format % tuple([action_to_record,r_to_record]))
visited_state_rule_counter += 1
else:
if save_new_data_RL:
visited_state_RL_value.append([action_to_record,r_to_record])
visited_state_RL_tree.insert(visited_state_RL_counter,
tuple((obs_to_record-visited_state_dist).tolist()[0]+(obs_to_record+visited_state_dist).tolist()[0]))
visited_state_RL_outfile.write(visited_state_format % tuple(obs_to_record[0]))
visited_value_RL_outfile.write(visited_value_format % tuple([action_to_record,r_to_record]))
visited_state_RL_counter += 1
####### Recorded #####
obs = env.reset()
episode_rewards.append(0.0)
reset = True
############### Trainning Part Start #####################
if not is_training:
# don't need to train the model
continue
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights, batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, actions, rewards, obses_tp1, dones, weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes, new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(episode_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward", mean_100ep_reward)
logger.record_tabular("% time spent exploring", int(100 * exploration.value(t)))
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts and
num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log("Saving model due to mean reward increase: {} -> {}".format(
saved_mean_reward, mean_100ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
rew_str = str(mean_100ep_reward)
path = osp.expanduser("~/models/carlaok_checkpoint/"+saved_name+"_"+rew_str)
act.save(path)
#### close the file ####
visited_state_rule_outfile.close()
visited_value_rule_outfile.close()
recorded_replay_buffer_outfile.close()
if not is_training:
testing_record_outfile.close()
#### close the file ###
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(saved_mean_reward))
load_variables(model_file)
return act
