def learn_continuous_tasks_noisy(env,
q_func,
env_name,
dir_path,
time_stamp,
total_num_episodes,
num_actions_pad=33,
lr=1e-4,
grad_norm_clipping=10,
max_timesteps=int(1e8),
buffer_size=int(1e6),
train_freq=1,
batch_size=64,
print_freq=10,
learning_starts=1000,
gamma=0.99,
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=int(1e8),
num_cpu=16,
epsilon_greedy=False,
timesteps_std=1e6,
initial_std=0.4,
final_std=0.05,
eval_freq=100,
n_eval_episodes=10,
eval_std=0.01,
log_index=0,
log_prefix='q',
loss_type="L2",
model_file='./',
callback=None):
"""Train a branching deepq model to solve continuous control tasks via discretization.
Current assumptions in the implementation:
- for solving continuous control domains via discretization (can be adjusted to be compatible with naturally disceret-action domains using 'env.action_space.n')
- uniform number of sub-actions per action dimension (can be generalized to heterogeneous number of sub-actions across branches)
Parameters
-------
env : gym.Env
environment to train on
q_func: (tf.Variable, int, str, bool) -> tf.Variable
the model that takes the following inputs:
observation_in: object
the output of observation placeholder
num_actions: int
number of actions
scope: str
reuse: bool
should be passed to outer variable scope
and returns a tensor of shape (batch_size, num_actions) with values of every action.
num_actions_pad: int
number of sub-actions per action dimension (= num of discretization grains/bars + 1)
lr: float
learning rate for adam optimizer
max_timesteps: int
number of env steps to optimize for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
0.1 for dqn-baselines
exploration_final_eps: float
final value of random action probability
0.02 for dqn-baselines
train_freq: int
update the model every `train_freq` steps.
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
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
grad_norm_clipping: int
set None for no clipping
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 max_timesteps.
prioritized_replay_eps: float
epsilon to add to the unified TD error for updating priorities.
Erratum: The camera-ready copy of this paper incorrectly reported 1e-8.
The value used to produece the results is 1e8.
num_cpu: int
number of cpus to use for training
dir_path: str
path for logs and results to be stored in
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
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.
"""
sess = U.make_session(num_cpu=num_cpu)
sess.__enter__()
def make_obs_ph(name):
return U.BatchInput(env.observation_space.shape, name=name)
print('Observation shape:' + str(env.observation_space.shape))
num_action_grains = num_actions_pad - 1
num_action_dims = env.action_space.shape[0]
num_action_streams = num_action_dims
num_actions = num_actions_pad * num_action_streams # total numb network outputs for action branching with one action dimension per branch
print('Number of actions in total:' + str(num_actions))
act, q_val, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=num_actions,
num_action_streams=num_action_streams,
batch_size=batch_size,
optimizer_name="Adam",
learning_rate=lr,
grad_norm_clipping=grad_norm_clipping,
gamma=gamma,
double_q=True,
scope="deepq",
reuse=None,
loss_type="L2")
print('TRAIN VARS:')
print(tf.trainable_variables())
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': num_actions,
'num_action_streams': num_action_streams,
}
print('Create the log writer for TensorBoard visualizations.')
log_dir = "{}/tensorboard_logs/{}".format(dir_path, env_name)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
score_placeholder = tf.placeholder(tf.float32, [],
name='score_placeholder')
tf.summary.scalar('score', score_placeholder)
lr_constant = tf.constant(lr, name='lr_constant')
tf.summary.scalar('learning_rate', lr_constant)
eval_placeholder = tf.placeholder(tf.float32, [], name='eval_placeholder')
eval_summary = tf.summary.scalar('evaluation', eval_placeholder)
# 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 = max_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
if epsilon_greedy:
approximate_num_iters = 2e6 / 4
exploration = PiecewiseSchedule([(0, 0.1),
(approximate_num_iters / 50, 0.01),
(approximate_num_iters / 5, 0.01)],
outside_value=0.01)
else:
exploration = ConstantSchedule(value=0.0) # greedy policy
std_schedule = LinearSchedule(schedule_timesteps=timesteps_std,
initial_p=initial_std,
final_p=final_std)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
# Initialize the parameters used for converting branching, discrete action indeces to continuous actions
low = env.action_space.low
high = env.action_space.high
actions_range = np.subtract(high, low)
print('###################################')
print(low)
print(high)
print('###################################')
episode_rewards = []
reward_sum = 0.0
time_steps = [0]
time_spent_exploring = [0]
prev_time = time.time()
n_trainings = 0
# Open a dircetory for recording results
results_dir = "{}/results/{}".format(dir_path, env_name)
if not os.path.exists(results_dir):
os.makedirs(results_dir)
displayed_mean_reward = None
score_timesteps = []
game_scores = []
def evaluate(step, episode_number):
global max_eval_reward_mean, model_saved
print('Evaluate...')
eval_reward_sum = 0.0
# Run evaluation episodes
for eval_episode in range(n_eval_episodes):
obs = env.reset()
done = False
while not done:
# Choose action
action_idxes = np.array(
act(np.array(obs)[None],
stochastic=False)) # deterministic
actions_greedy = action_idxes / num_action_grains * actions_range + low
if eval_std == 0.0:
action = actions_greedy
else:
action = []
for index in range(len(actions_greedy)):
a_greedy = actions_greedy[index]
out_of_range_action = True
while out_of_range_action:
a_stoch = np.random.normal(loc=a_greedy,
scale=eval_std)
a_idx_stoch = np.rint(
(a_stoch + high[index]) /
actions_range[index] * num_action_grains)
if a_idx_stoch >= 0 and a_idx_stoch < num_actions_pad:
action.append(a_stoch)
out_of_range_action = False
# Step
obs, rew, done, _ = env.step(action)
eval_reward_sum += rew
# Average the rewards and log
eval_reward_mean = eval_reward_sum / n_eval_episodes
print(eval_reward_mean, 'over', n_eval_episodes, 'episodes')
game_scores.append(eval_reward_mean)
score_timesteps.append(step)
if max_eval_reward_mean is None or eval_reward_mean > max_eval_reward_mean:
logger.log(
"Saving model due to mean eval increase: {} -> {}".format(
max_eval_reward_mean, eval_reward_mean))
U.save_state(model_file)
model_saved = True
max_eval_reward_mean = eval_reward_mean
intact = ActWrapper(act, act_params)
intact.save(model_file + "_" + str(episode_number) + "_" +
str(int(np.round(max_eval_reward_mean))))
print('Act saved to ' + model_file + "_" + str(episode_number) +
"_" + str(int(np.round(max_eval_reward_mean))))
with tempfile.TemporaryDirectory() as td:
td = './logs'
evaluate(0, 0)
obs = env.reset()
t = -1
all_means = []
q_stats = []
current_qs = []
training_game_scores = []
training_timesteps = []
weights_mean = []
weights_stds = []
names = []
for joint_i in range(num_action_streams):
names.append([])
names[-1].append("deepq/q_func/action_value/" + str(joint_i) +
"128/w_sigma:0")
names[-1].append("deepq/q_func/action_value/" + str(joint_i) +
"out/w_sigma:0")
mus = []
for nl in names:
mus.append([])
for n in nl:
for v in tf.trainable_variables():
if v.name == n:
mus[-1].append(v)
variables_names = [v.name for v in tf.trainable_variables()]
print(variables_names)
print(mus)
while True:
t += 1
# Select action and update exploration probability
action_idxes = np.array(
act(np.array(obs)[None], update_eps=exploration.value(t)))
qs = np.array(q_val(np.array(obs)[None],
stochastic=False)) # deterministic
tt = []
for val in qs:
tt.append(np.std(val))
current_qs.append(tt)
# Convert sub-actions indexes (discrete sub-actions) to continuous controls
action = action_idxes / num_action_grains * actions_range + low
if not epsilon_greedy: # Gaussian noise
actions_greedy = action
action_idx_stoch = []
action = []
for index in range(len(actions_greedy)):
a_greedy = actions_greedy[index]
out_of_range_action = True
while out_of_range_action:
# Sample from a Gaussian with mean at the greedy action and a std following a schedule of choice
a_stoch = np.random.normal(loc=a_greedy,
scale=std_schedule.value(t))
# Convert sampled cont action to an action idx
a_idx_stoch = np.rint(
(a_stoch + high[index]) / actions_range[index] *
num_action_grains)
# Check if action is in range
if a_idx_stoch >= 0 and a_idx_stoch < num_actions_pad:
action_idx_stoch.append(a_idx_stoch)
action.append(a_stoch)
out_of_range_action = False
action_idxes = action_idx_stoch
new_obs, rew, done, _ = env.step(np.array(action))
# Store transition in the replay buffer
replay_buffer.add(obs, action_idxes, rew, new_obs, float(done))
obs = new_obs
reward_sum += rew
if done:
obs = env.reset()
time_spent_exploring[-1] = int(100 * exploration.value(t))
time_spent_exploring.append(0)
episode_rewards.append(reward_sum)
training_game_scores.append(reward_sum)
training_timesteps.append(t)
time_steps[-1] = t
reward_sum = 0.0
time_steps.append(0)
q_stats.append(np.mean(current_qs, 0))
current_qs = []
t_m = []
ts_m = []
for mu_mod in mus:
tt = []
tts = []
for mu_l in mu_mod:
muw = np.ravel(sess.run(mu_l))
tt.append(np.mean(muw))
tts.append(np.std(muw))
t_m.append(tt)
ts_m.append(tts)
weights_mean.append(t_m)
weights_stds.append(ts_m)
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) # np.ones_like(rewards)) #TEMP AT NEW
# print(np.mean(td_errors))
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes,
new_priorities)
n_trainings += 1
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically
update_target()
if len(episode_rewards) == 0:
mean_100ep_reward = 0
elif len(episode_rewards) < 100:
mean_100ep_reward = np.mean(episode_rewards)
else:
mean_100ep_reward = np.mean(episode_rewards[-100:])
all_means.append(mean_100ep_reward)
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)))
current_time = time.time()
logger.record_tabular("trainings per second",
n_trainings / (current_time - prev_time))
logger.dump_tabular()
n_trainings = 0
prev_time = current_time
if t > learning_starts and num_episodes > 100:
if displayed_mean_reward is None or mean_100ep_reward > displayed_mean_reward:
if print_freq is not None:
logger.log("Mean reward increase: {} -> {}".format(
displayed_mean_reward, mean_100ep_reward))
displayed_mean_reward = mean_100ep_reward
# Performance evaluation with a greedy policy
if done and num_episodes % eval_freq == 0:
evaluate(t + 1, num_episodes)
obs = env.reset()
pickle.dump(q_stats,
open(
str(log_index) + "q_stat_stds99_" + log_prefix +
".pkl", 'wb'),
protocol=pickle.HIGHEST_PROTOCOL)
pickle.dump(weights_mean,
open(
str(log_index) + "q_stat_wmu99_" + log_prefix + ".pkl",
'wb'),
protocol=pickle.HIGHEST_PROTOCOL)
pickle.dump(weights_stds,
open(
str(log_index) + "q_stat_wsig99_" + log_prefix +
".pkl", 'wb'),
protocol=pickle.HIGHEST_PROTOCOL)
pickle.dump(game_scores,
open(
str(log_index) + "q_stat_scores99_" + log_prefix +
".pkl", 'wb'),
protocol=pickle.HIGHEST_PROTOCOL)
return ActWrapper(act, act_params)
def __init__(self, args, env, writer=None):
"""
init agent
"""
self.eval_env = copy.deepcopy(env)
self.args = args
self.state_dim = env.reset().shape
self.action_dim = env.action_space.n
self.device = torch.device("cuda" if (
torch.cuda.is_available() and self.args.gpu) else "cpu")
# set the random seed in the main launcher
random.seed(self.args.seed)
torch.manual_seed(self.args.seed)
np.random.seed(self.args.seed)
if self.args.gpu:
torch.cuda.manual_seed(self.args.seed)
self.writer = writer
if self.args.env_name == "grid":
self.dqn = OneHotDQN(self.state_dim,
self.action_dim).to(self.device)
self.dqn_target = OneHotDQN(self.state_dim,
self.action_dim).to(self.device)
# create more networks here
self.cost_model = OneHotDQN(self.state_dim,
self.action_dim).to(self.device)
self.review_model = OneHotValueNetwork(self.state_dim).to(
self.device)
self.target_cost_model = OneHotDQN(self.state_dim,
self.action_dim).to(self.device)
self.target_review_model = OneHotValueNetwork(self.state_dim).to(
self.device)
self.target_cost_model.load_state_dict(
self.cost_model.state_dict())
self.target_review_model.load_state_dict(
self.review_model.state_dict())
else:
raise Exception("Not implemented yet!")
# copy parameters
self.dqn_target.load_state_dict(self.dqn.state_dict())
self.optimizer = torch.optim.Adam(self.dqn.parameters(),
lr=self.args.lr)
self.review_optimizer = optim.Adam(self.review_model.parameters(),
lr=self.args.cost_reverse_lr)
self.critic_optimizer = optim.Adam(self.cost_model.parameters(),
lr=self.args.cost_q_lr)
# parallel agents
def make_env():
def _thunk():
env = create_env(args)
return env
return _thunk
envs = [make_env() for i in range(self.args.num_envs)]
self.envs = SubprocVecEnv(envs)
# create epsilon and beta schedule
self.eps_decay = LinearSchedule(50000 * 200, 0.01, 1.0)
# self.eps_decay = LinearSchedule(self.args.num_episodes * 200, 0.01, 1.0)
self.total_steps = 0
self.num_episodes = 0
# for storing resutls
self.results_dict = {
"train_rewards": [],
"train_constraints": [],
"eval_rewards": [],
"eval_constraints": [],
}
self.cost_indicator = "none"
if "grid" in self.args.env_name:
self.cost_indicator = 'pit'
else:
raise Exception("not implemented yet")
self.eps = self.eps_decay.value(self.total_steps)
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
class Learn:
def __init__(self, config, env):
super().__init__()
self.config = config
self.env = env
self.agent_ids = self.get_agent_ids()
self.replay_memory, self.beta_schedule = self.init_replay_memory()
self.optimizer = tf.keras.optimizers.Adam(self.config.lr)
# Create the schedule for exploration starting from 1.
self.exploration = LinearSchedule(schedule_timesteps=int(config.exploration_fraction * config.num_timesteps),
initial_p=1.0, final_p=config.exploration_final_eps)
self.eps = tf.Variable(0.0)
self.models, self.target_models = self._init_networks()
self.agents = [Agent(config, self.models[agent_id],
self.target_models[agent_id], agent_id) for agent_id in self.agent_ids]
def _init_networks(self):
network = Network(self.config, self.agent_ids)
# base_model = network.init_base_model()
# target_base_model = network.init_base_model()
return network.build_models(), network.build_models()
def get_agent_ids(self):
return [agent_id for agent_id in range(self.config.num_agents)]
def init_replay_memory(self):
"""
:return: replay_buffer, beta_schedule
"""
if self.config.prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(self.config.buffer_size, alpha=self.config.prioritized_replay_alpha)
if self.config.prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = self.config.num_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=self.config.prioritized_replay_beta0, final_p=1.0)
else:
replay_buffer = ReplayBuffer(self.config.buffer_size)
beta_schedule = None
return replay_buffer, beta_schedule
@tf.function
def get_actions(self, obs, stochastic=True, update_eps=-1):
"""
:param obs: observation for all agents
:param stochastic: True for Train phase and False for test phase
:param update_eps: epsilon update for eps-greedy
:return: actions, q_values of all agents as fps
"""
deterministic_actions = []
fps = []
for agent_id in self.agent_ids:
deterministic_action, fp = self.agents[agent_id].greedy_action(obs[agent_id])
deterministic_actions.append(deterministic_action)
fps.append(fp)
# print(f' deterministic_actions {deterministic_actions}')
# print(f' fps {fps}')
random_actions = tf.random.uniform(tf.stack([self.config.num_agents]), minval=0,
maxval=self.config.num_actions, dtype=tf.int64)
# print(f' random_actions {random_actions}')
chose_random = tf.random.uniform(tf.stack([self.config.num_agents]), minval=0,
maxval=1, dtype=tf.float32) < self.eps
# print(f' chose_random {chose_random}')
stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)
# print(f' stochastic_actions.numpy() {stochastic_actions.numpy()}')
if stochastic:
actions = stochastic_actions.numpy()
else:
actions = deterministic_actions
if update_eps >= 0:
self.eps.assign(update_eps)
# print(f' actions {actions}')
return actions, fps
@tf.function
def get_max_values(self, obs):
"""
:param obs: list observations one for each agent
:return: best values based on Q-Learning formula maxQ(s',a')
"""
best_q_vals = []
for agent_id in self.agent_ids:
best_q_val = self.agents[agent_id].max_value(obs[agent_id])
best_q_vals.append(best_q_val)
# print(f' best_q_vals.numpy() {best_q_vals.numpy()}')
return best_q_vals
@tf.function
def compute_loss(self, obses_t, actions, rewards, dones, weights, fps=None):
"""
:param obses_t: list observations one for each agent
:param actions:
:param rewards:
:param dones:
:param weights:
:param fps:
:return: loss and td errors tensor list one for each agent
"""
losses = []
td_errors = np.zeros(self.config.batch_size)
for agent_id in self.agent_ids:
loss, td_error = self.agents[agent_id].compute_loss(obses_t[agent_id], actions[agent_id],
rewards[agent_id], dones[agent_id],
weights[agent_id], fps=None)
losses.append(loss)
td_errors += td_error
return losses, td_errors
@tf.function()
def train(self, obses_t, actions, rewards, dones, weights, fps=None):
with tf.GradientTape() as tape:
losses, td_errors = self.compute_loss(obses_t, actions, rewards, dones, weights, fps)
loss = tf.reduce_sum(losses)
params = tape.watched_variables()
# print(f' param {params}')
grads = tape.gradient(loss, params)
if self.config.grad_norm_clipping:
clipped_grads = []
for grad in grads:
clipped_grads.append(tf.clip_by_norm(grad, self.config.grad_norm_clipping))
grads = clipped_grads
self.optimizer.apply_gradients(list(zip(grads, params)))
return loss, td_errors
def create_fingerprints(self, fps, t):
# TODO
fps = []
if self.config.num_agents > 1:
for agent_id in self.agent_ids:
fp = fps[:agent_id]
fp.extend(fps[agent_id + 1:])
fp_a = np.concatenate((fp, [[self.exploration.value(t) * 100, t]]), axis=None)
fps.append(fp_a)
return fps
def learn(self):
episode_rewards = [0.0]
obs = self.env.reset()
print(obs.shape)
done = False
tstart = time.time()
episodes_trained = [0, False] # [episode_number, Done flag]
for t in range(self.config.num_timesteps):
# if t == 102:
# break
update_eps = tf.constant(self.exploration.value(t))
mb_obs, mb_rewards, mb_actions, mb_obs1, mb_dones = [], [], [], [], []
for n_step in range(self.config.n_steps):
# print(f't is {t} -- n_steps is {n_step}')
actions, _ = self.get_actions(tf.constant(obs), update_eps=update_eps)
if self.config.num_agents == 1:
obs1, rews, done, _ = self.env.step(actions[0])
else:
obs1, rews, done, _ = self.env.step(actions)
# TODO fingerprint computation
mb_obs.append(obs.copy())
mb_actions.append(actions)
mb_dones.append([float(done) for _ in self.agent_ids])
# print(f'rewards is {rews}')
if self.config.same_reward_for_agents:
rews = [np.max(rews) for _ in range(len(rews))] # for cooperative purpose same reward for every one
mb_obs1.append(obs1.copy())
mb_rewards.append(rews)
obs = obs1
episode_rewards[-1] += np.max(rews)
if done:
episodes_trained[0] = episodes_trained[0] + 1
episodes_trained[1] = True
episode_rewards.append(0.0)
obs = self.env.reset()
mb_dones.append([float(done) for _ in self.agent_ids])
# swap axes to have lists in shape of (num_agents, num_steps, ...)
# print(f' mb_obs.shape is {np.array(mb_obs).shape}')
mb_obs = np.asarray(mb_obs, dtype=obs[0].dtype).swapaxes(0, 1)
# print(f' mb_obs.shape is {np.array(mb_obs).shape}')
mb_actions = np.asarray(mb_actions, dtype=actions[0].dtype).swapaxes(0, 1)
mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(0, 1)
mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(0, 1)
mb_masks = mb_dones[:, :-1]
mb_dones = mb_dones[:, 1:]
# print(f' before discount mb_rewards is {mb_rewards}')
if self.config.gamma > 0.0:
# print(f' last_values {last_values}')
for agent_id, (rewards, dones) in enumerate(zip(mb_rewards, mb_dones)):
value = self.agents[agent_id].max_value(tf.constant(obs1[agent_id]))
rewards = rewards.tolist()
dones = dones.tolist()
if dones[-1] == 0:
rewards = discount_with_dones(rewards + [value], dones + [0], self.config.gamma)[:-1]
else:
rewards = discount_with_dones(rewards, dones, self.config.gamma)
mb_rewards[agent_id] = rewards
# print(f' after discount mb_rewards is {mb_rewards}')
if self.config.replay_buffer is not None:
self.replay_memory.add(mb_obs, mb_actions, mb_rewards, mb_obs1, mb_masks)
if t > self.config.learning_starts and t % self.config.train_freq == 0:
if self.config.prioritized_replay:
experience = self.replay_memory.sample(self.config.batch_size, beta=self.beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights, batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = self.replay_memory.sample(self.config.batch_size)
weights, batch_idxes = np.ones_like(rewards), None
# print(f'obses_t.shape {obses_t.shape}')
# shape format is (batch_size, agent_num, n_steps, ...)
obses_t = obses_t.swapaxes(0, 1)
actions = actions.swapaxes(0, 1)
rewards = rewards.swapaxes(0, 1)
# print(f'rewards.shape {rewards.shape}')
obses_tp1 = obses_tp1.swapaxes(0, 1)
dones = dones.swapaxes(0, 1)
print(f'weights.shape {weights.shape}')
weights = weights.swapaxes(0, 1) # weights shape is (1, batch_size, n_steps)
print(f'weights.shape {weights.shape}')
# shape format is (agent_num, batch_size, n_steps, ...)
if 'rnn' not in self.config.network:
shape = obses_t.shape
obses_t = np.reshape(obses_t, (shape[0], shape[1] * shape[2], *shape[3:]))
shape = actions.shape
actions = np.reshape(actions, (shape[0], shape[1] * shape[2], *shape[3:]))
shape = rewards.shape
rewards = np.reshape(rewards, (shape[0], shape[1] * shape[2], *shape[3:]))
shape = dones.shape
dones = np.reshape(dones, (shape[0], shape[1] * shape[2], *shape[3:]))
shape = weights.shape
weights = np.reshape(weights, (shape[0], shape[1]))
# print(f'obses_t.shape {obses_t.shape}')
# shape format is (agent_num, batch_size * n_steps, ...)
obses_t = tf.constant(obses_t)
actions = tf.constant(actions)
rewards = tf.constant(rewards)
dones = tf.constant(dones)
weights = tf.constant(weights)
# print(f' obses_t.shape {obses_t.shape}')
# print(f' actions.shape {actions.shape}')
# print(f' rewards.shape {rewards.shape}')
# print(f' dones.shape {dones.shape}')
# print(f' weights.shape {weights.shape}')
loss, td_errors = self.train(obses_t, actions, rewards, dones, weights)
# print(f'td_errors.shape = {np.array(td_errors).shape} , batch_idxes.shape = {np.array(batch_idxes).shape}')
if self.config.prioritized_replay:
new_priorities = np.abs(td_errors) + self.config.prioritized_replay_eps
self.replay_memory.update_priorities(batch_idxes, new_priorities)
if t % (self.config.train_freq * 50) == 0:
print(f't = {t} , loss = {loss}')
if t > self.config.learning_starts and t % self.config.target_network_update_freq == 0:
# Update target network periodically.
for agent_id in self.agent_ids:
self.agents[agent_id].soft_update_target()
if t % self.config.playing_test == 0 and t != 0:
# self.network.save(self.config.save_path)
self.play_test_games()
mean_100ep_reward = np.mean(episode_rewards[-101:-1])
num_episodes = len(episode_rewards)
if t % (self.config.print_freq*100) == 0:
time_1000_step = time.time()
nseconds = time_1000_step - tstart
tstart = time_1000_step
print(f'eps {self.exploration.value(t)} -- time {t - self.config.print_freq*1000} to {t} steps: {nseconds}')
# if done and self.config.print_freq is not None and len(episode_rewards) % self.config.print_freq == 0:
if episodes_trained[1] and episodes_trained[0] % self.config.print_freq == 0:
episodes_trained[1] = False
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 past episode reward", mean_100ep_reward)
logger.record_tabular("% time spent exploring", int(100 * self.exploration.value(t)))
logger.dump_tabular()
def play_test_games(self):
num_tests = self.config.num_tests
test_env = init_env(self.config, mode='test')
test_rewards = np.zeros(num_tests)
for i in range(num_tests):
done = False
obs = test_env.reset()
iter = 0
while True:
iter += 1
actions, _ = self.get_actions(tf.constant(obs), stochastic=False)
# print(f'actions[0] {actions[0]}, test_done {done}, {iter}')
if self.config.num_agents == 1:
obs1, rews, done, _ = test_env.step(actions[0])
else:
obs1, rews, done, _ = test_env.step(actions)
# ToDo fingerprint computation
obs = obs1
if done or iter >= self.config.max_episodes_length:
# print(f'test {i} rewards is {rews}')
test_rewards[i] = np.mean(rews)
break
print(f'test_rewards: {test_rewards} \n mean reward of {num_tests} tests: {np.mean(test_rewards)}')
test_env.close()
class SafeSarsaAgent(object):
def __init__(self, args, env, writer=None):
"""
init agent
"""
self.eval_env = copy.deepcopy(env)
self.args = args
self.state_dim = env.reset().shape
self.action_dim = env.action_space.n
self.device = torch.device("cuda" if (
torch.cuda.is_available() and self.args.gpu) else "cpu")
# set the random seed in the main launcher
random.seed(self.args.seed)
torch.manual_seed(self.args.seed)
np.random.seed(self.args.seed)
if self.args.gpu:
torch.cuda.manual_seed(self.args.seed)
self.writer = writer
if self.args.env_name == "grid":
self.dqn = OneHotDQN(self.state_dim,
self.action_dim).to(self.device)
self.dqn_target = OneHotDQN(self.state_dim,
self.action_dim).to(self.device)
# create more networks here
self.cost_model = OneHotDQN(self.state_dim,
self.action_dim).to(self.device)
self.review_model = OneHotValueNetwork(self.state_dim).to(
self.device)
self.target_cost_model = OneHotDQN(self.state_dim,
self.action_dim).to(self.device)
self.target_review_model = OneHotValueNetwork(self.state_dim).to(
self.device)
self.target_cost_model.load_state_dict(
self.cost_model.state_dict())
self.target_review_model.load_state_dict(
self.review_model.state_dict())
else:
raise Exception("Not implemented yet!")
# copy parameters
self.dqn_target.load_state_dict(self.dqn.state_dict())
self.optimizer = torch.optim.Adam(self.dqn.parameters(),
lr=self.args.lr)
self.review_optimizer = optim.Adam(self.review_model.parameters(),
lr=self.args.cost_reverse_lr)
self.critic_optimizer = optim.Adam(self.cost_model.parameters(),
lr=self.args.cost_q_lr)
# parallel agents
def make_env():
def _thunk():
env = create_env(args)
return env
return _thunk
envs = [make_env() for i in range(self.args.num_envs)]
self.envs = SubprocVecEnv(envs)
# create epsilon and beta schedule
self.eps_decay = LinearSchedule(50000 * 200, 0.01, 1.0)
# self.eps_decay = LinearSchedule(self.args.num_episodes * 200, 0.01, 1.0)
self.total_steps = 0
self.num_episodes = 0
# for storing resutls
self.results_dict = {
"train_rewards": [],
"train_constraints": [],
"eval_rewards": [],
"eval_constraints": [],
}
self.cost_indicator = "none"
if "grid" in self.args.env_name:
self.cost_indicator = 'pit'
else:
raise Exception("not implemented yet")
self.eps = self.eps_decay.value(self.total_steps)
def pi(self, state, current_cost=0.0, greedy_eval=False):
"""
take the action based on the current policy
"""
with torch.no_grad():
# to take random action or not
if (random.random() > self.eps_decay.value(
self.total_steps)) or greedy_eval:
q_value = self.dqn(state)
# chose the max/greedy actions
action = q_value.max(1)[1].cpu().numpy()
else:
action = np.random.randint(0,
high=self.action_dim,
size=(self.args.num_envs, ))
return action
def safe_deterministic_pi(self,
state,
current_cost=0.0,
greedy_eval=False):
"""
take the action based on the current policy
"""
with torch.no_grad():
# to take random action or not
if (random.random() > self.eps_decay.value(
self.total_steps)) or greedy_eval:
# No random action
q_value = self.dqn(state)
# Q_D(s,a)
cost_q_val = self.cost_model(state)
cost_r_val = self.review_model(state)
# find the action set
# create the filtered mask here
constraint_mask = torch.le(cost_q_val + cost_r_val,
self.args.d0 +
current_cost).float()
filtered_Q = (q_value + 1000.0) * (constraint_mask)
filtered_action = filtered_Q.max(1)[1].cpu().numpy()
# alt action to take if infeasible solution
# minimize the cost
alt_action = (-1. * cost_q_val).max(1)[1].cpu().numpy()
c_sum = constraint_mask.sum(1)
action_mask = (c_sum == torch.zeros_like(c_sum)).cpu().numpy()
action = (1 - action_mask
) * filtered_action + action_mask * alt_action
return action
else:
# create an array of random indices, for all the environments
action = np.random.randint(0,
high=self.action_dim,
size=(self.args.num_envs, ))
return action
def compute_n_step_returns(self, next_value, rewards, masks):
"""
n-step SARSA returns
"""
R = next_value
returns = []
for step in reversed(range(len(rewards))):
R = rewards[step] + self.args.gamma * R * masks[step]
returns.insert(0, R)
return returns
def compute_reverse_n_step_returns(self, prev_value, costs, begin_masks):
"""
n-step SARSA returns (backward in time)
"""
R = prev_value
returns = []
for step in range(len(costs)):
R = costs[step] + self.args.gamma * R * begin_masks[step]
returns.append(R)
return returns
def log_episode_stats(self, ep_reward, ep_constraint):
"""
log the stats for environment performance
"""
# log episode statistics
self.results_dict["train_rewards"].append(ep_reward)
self.results_dict["train_constraints"].append(ep_constraint)
if self.writer:
self.writer.add_scalar("Return", ep_reward, self.num_episodes)
self.writer.add_scalar("Constraint", ep_constraint,
self.num_episodes)
log(
'Num Episode {}\t'.format(self.num_episodes) + \
'E[R]: {:.2f}\t'.format(ep_reward) +\
'E[C]: {:.2f}\t'.format(ep_constraint) +\
'avg_train_reward: {:.2f}\t'.format(np.mean(self.results_dict["train_rewards"][-100:])) +\
'avg_train_constraint: {:.2f}\t'.format(np.mean(self.results_dict["train_constraints"][-100:]))
)
def run(self):
"""
learning happens here
"""
self.total_steps = 0
self.eval_steps = 0
# reset state and env
state = self.envs.reset()
prev_state = torch.FloatTensor(state).to(self.device)
current_cost = torch.zeros(self.args.num_envs,
1).float().to(self.device)
ep_reward = 0
ep_len = 0
ep_constraint = 0
start_time = time.time()
while self.num_episodes < self.args.num_episodes:
values = []
c_q_vals = []
c_r_vals = []
states = []
actions = []
mus = []
prev_states = []
rewards = []
done_masks = []
begin_masks = []
constraints = []
# n-step sarsa
for _ in range(self.args.traj_len):
state = torch.FloatTensor(state).to(self.device)
# get the action
action = self.safe_deterministic_pi(state,
current_cost=current_cost)
next_state, reward, done, info = self.envs.step(action)
# convert it back to tensor
action = torch.LongTensor(action).unsqueeze(1).to(self.device)
q_values = self.dqn(state)
Q_value = q_values.gather(1, action)
c_q_values = self.cost_model(state)
cost_q_val = c_q_values.gather(1, action)
cost_r_val = self.review_model(state)
# logging mode for only agent 1
ep_reward += reward[0]
ep_constraint += info[0][self.cost_indicator]
values.append(Q_value)
c_r_vals.append(cost_r_val)
c_q_vals.append(cost_q_val)
rewards.append(
torch.FloatTensor(reward).unsqueeze(1).to(self.device))
done_masks.append(
torch.FloatTensor(1.0 - done).unsqueeze(1).to(self.device))
begin_masks.append(
torch.FloatTensor([(1.0 - ci['begin']) for ci in info
]).unsqueeze(1).to(self.device))
constraints.append(
torch.FloatTensor([ci[self.cost_indicator] for ci in info
]).unsqueeze(1).to(self.device))
prev_states.append(prev_state)
states.append(state)
actions.append(action)
# update the state
prev_state = state
state = next_state
# update the current cost
# if done flag is true for the current env, this implies that the next_state cost = 0.0
# because the agent starts with 0.0 cost (or doesn't have access to it anyways)
current_cost = torch.FloatTensor([
(ci[self.cost_indicator] * (1.0 - di))
for ci, di in zip(info, done)
]).unsqueeze(1).to(self.device)
self.total_steps += (1 * self.args.num_envs)
# hack to reuse the same code
# iteratively add each done episode, so that can eval at regular interval
for d_idx in range(done.sum()):
if done[0] and d_idx == 0:
if self.num_episodes % self.args.log_every == 0:
self.log_episode_stats(ep_reward, ep_constraint)
# reset the rewards anyways
ep_reward = 0
ep_constraint = 0
self.num_episodes += 1
# eval the policy here after eval_every steps
if self.num_episodes % self.args.eval_every == 0:
eval_reward, eval_constraint = self.eval()
self.results_dict["eval_rewards"].append(eval_reward)
self.results_dict["eval_constraints"].append(
eval_constraint)
log('----------------------------------------')
log('Eval[R]: {:.2f}\t'.format(eval_reward) +\
'Eval[C]: {}\t'.format(eval_constraint) +\
'Episode: {}\t'.format(self.num_episodes) +\
'avg_eval_reward: {:.2f}\t'.format(np.mean(self.results_dict["eval_rewards"][-10:])) +\
'avg_eval_constraint: {:.2f}\t'.format(np.mean(self.results_dict["eval_constraints"][-10:]))
)
log('----------------------------------------')
if self.writer:
self.writer.add_scalar("eval_reward", eval_reward,
self.eval_steps)
self.writer.add_scalar("eval_constraint",
eval_constraint,
self.eval_steps)
self.eval_steps += 1
# break here
if self.num_episodes >= self.args.num_episodes:
break
# calculate targets here
next_state = torch.FloatTensor(next_state).to(self.device)
next_q_values = self.dqn(next_state)
next_action = self.safe_deterministic_pi(next_state, current_cost)
next_action = torch.LongTensor(next_action).unsqueeze(1).to(
self.device)
next_q_values = next_q_values.gather(1, next_action)
# calculate targets
target_Q_vals = self.compute_n_step_returns(
next_q_values, rewards, done_masks)
Q_targets = torch.cat(target_Q_vals).detach()
Q_values = torch.cat(values)
# loss
loss = F.mse_loss(Q_values, Q_targets)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# calculate the cost-targets
next_c_value = self.cost_model(next_state)
next_c_value = next_c_value.gather(1, next_action)
cq_targets = self.compute_n_step_returns(next_c_value, constraints,
done_masks)
C_q_targets = torch.cat(cq_targets).detach()
C_q_vals = torch.cat(c_q_vals)
cost_critic_loss = F.mse_loss(C_q_vals, C_q_targets)
self.critic_optimizer.zero_grad()
cost_critic_loss.backward()
self.critic_optimizer.step()
# For the constraints (reverse)
prev_value = self.review_model(prev_states[0])
c_r_targets = self.compute_reverse_n_step_returns(
prev_value, constraints, begin_masks)
C_r_targets = torch.cat(c_r_targets).detach()
C_r_vals = torch.cat(c_r_vals)
cost_review_loss = F.mse_loss(C_r_vals, C_r_targets)
self.review_optimizer.zero_grad()
cost_review_loss.backward()
self.review_optimizer.step()
# done with all the training
# save the models
self.save_models()
def eval(self):
"""
evaluate the current policy and log it
"""
avg_reward = []
avg_constraint = []
with torch.no_grad():
for _ in range(self.args.eval_n):
state = self.eval_env.reset()
done = False
ep_reward = 0
ep_constraint = 0
ep_len = 0
start_time = time.time()
current_cost = torch.FloatTensor([0.0]).to(
self.device).unsqueeze(0)
while not done:
# convert the state to tensor
state = torch.FloatTensor(state).to(
self.device).unsqueeze(0)
# get the policy action
action = self.safe_deterministic_pi(
state, current_cost=current_cost, greedy_eval=True)[0]
next_state, reward, done, info = self.eval_env.step(action)
ep_reward += reward
ep_len += 1
ep_constraint += info[self.cost_indicator]
# update the state
state = next_state
current_cost = torch.FloatTensor([
info[self.cost_indicator] * (1.0 - done)
]).to(self.device).unsqueeze(0)
avg_reward.append(ep_reward)
avg_constraint.append(ep_constraint)
return np.mean(avg_reward), np.mean(avg_constraint)
def save_models(self):
"""create results dict and save"""
models = {
"dqn": self.dqn.state_dict(),
"cost_model": self.cost_model.state_dict(),
"review_model": self.review_model.state_dict(),
"env": copy.deepcopy(self.eval_env),
}
torch.save(models, os.path.join(self.args.out, 'models.pt'))
# save the results
torch.save(self.results_dict,
os.path.join(self.args.out, 'results_dict.pt'))
def load_models(self):
models = torch.load(os.path.join(self.args.out, 'models.pt'))
self.dqn.load_state_dict(models["dqn"])
self.eval_env = models["env"]
def learn(env_id,
q_func,
lr=5e-4,
max_timesteps=10000,
buffer_size=5000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
train_steps=10,
learning_starts=500,
batch_size=32,
print_freq=10,
checkpoint_freq=100,
model_dir=None,
gamma=1.0,
target_network_update_freq=50,
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,
player_processes=None,
player_connections=None):
env, _, _ = create_gvgai_environment(env_id)
# Create all the functions necessary to train the model
# expert_decision_maker = ExpertDecisionMaker(env=env)
# 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)
session = tf.Session()
session.__enter__()
policy_path = os.path.join(model_dir, "Policy.pkl")
model_path = os.path.join(model_dir, "model", "model")
if os.path.isdir(os.path.join(model_dir, "model")):
load_state(model_path)
else:
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
act.save(policy_path)
save_state(model_path)
env.close()
# Create the replay buffer
if prioritized_replay:
replay_buffer_path = os.path.join(model_dir, "Prioritized_replay.pkl")
if os.path.isfile(replay_buffer_path):
with open(replay_buffer_path, 'rb') as input_file:
replay_buffer = pickle.load(input_file)
else:
replay_buffer = PrioritizedReplayBuffer(
buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = max_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer_path = os.path.join(model_dir, "Normal_replay.pkl")
if os.path.isfile(replay_buffer_path):
with open(replay_buffer_path, 'rb') as input_file:
replay_buffer = pickle.load(input_file)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
max_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
episode_rewards = list()
saved_mean_reward = -999999999
signal.signal(signal.SIGQUIT, signal_handler)
global terminate_learning
total_timesteps = 0
for timestep in range(max_timesteps):
if terminate_learning:
break
for connection in player_connections:
experiences, reward = connection.recv()
episode_rewards.append(reward)
for experience in experiences:
replay_buffer.add(*experience)
total_timesteps += 1
if total_timesteps < learning_starts:
if timestep % 10 == 0:
print("not strated yet", flush=True)
continue
if timestep % train_freq == 0:
for i in range(train_steps):
# 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(total_timesteps))
(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 timestep % 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 print_freq is not None and timestep % print_freq == 0:
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(total_timesteps)))
logger.dump_tabular()
if timestep % checkpoint_freq == 0 and mean_100ep_reward > saved_mean_reward:
act.save(policy_path)
save_state(model_path)
saved_mean_reward = mean_100ep_reward
with open(replay_buffer_path, 'wb') as output_file:
pickle.dump(replay_buffer, output_file,
pickle.HIGHEST_PROTOCOL)
send_message_to_all(player_connections, Message.UPDATE)
send_message_to_all(player_connections, Message.TERMINATE)
if mean_100ep_reward > saved_mean_reward:
act.save(policy_path)
with open(replay_buffer_path, 'wb') as output_file:
pickle.dump(replay_buffer, output_file, pickle.HIGHEST_PROTOCOL)
for player_process in player_processes:
player_process.join()
# player_process.terminate()
return act.load(policy_path)
def learn_continuous_tasks(env,
q_func,
env_name,
time_stamp,
total_num_episodes,
num_actions_pad=33,
lr=1e-4,
grad_norm_clipping=10,
max_timesteps=int(1e8),
buffer_size=int(1e6),
train_freq=1,
batch_size=64,
print_freq=10,
learning_starts=1000,
gamma=0.99,
target_network_update_freq=500,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=2e6,
prioritized_replay_eps=int(1e8),
num_cpu=16,
timesteps_std=1e6,
initial_std=0.4,
final_std=0.05,
eval_freq=100,
n_eval_episodes=10,
eval_std=0.01,
callback=None):
"""Train a branching deepq model to solve continuous control tasks via discretization.
Current assumptions in the implementation:
- for solving continuous control domains via discretization (can be adjusted to be compatible with naturally disceret-action domains using 'env.action_space.n')
- uniform number of sub-actions per action dimension (can be generalized to heterogeneous number of sub-actions across branches)
Parameters
-------
env : gym.Env
environment to train on
q_func: (tf.Variable, int, str, bool) -> tf.Variable
the model that takes the following inputs:
observation_in: object
the output of observation placeholder
num_actions: int
number of actions
scope: str
reuse: bool
should be passed to outer variable scope
and returns a tensor of shape (batch_size, num_actions) with values of every action.
num_actions_pad: int
number of sub-actions per action dimension (= num of discretization grains/bars + 1)
lr: float
learning rate for adam optimizer
max_timesteps: int
number of env steps to optimize for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
0.1 for dqn-baselines
exploration_final_eps: float
final value of random action probability
0.02 for dqn-baselines
train_freq: int
update the model every `train_freq` steps.
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
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
grad_norm_clipping: int
set None for no clipping
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 max_timesteps.
prioritized_replay_eps: float
epsilon to add to the unified TD error for updating priorities.
Erratum: The camera-ready copy of this paper incorrectly reported 1e-8.
The value used to produece the results is 1e8.
num_cpu: int
number of cpus to use for training
losses_version: int
optimization version number
dir_path: str
path for logs and results to be stored in
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
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.
"""
sess = U.make_session(num_cpu=num_cpu)
sess.__enter__()
def make_obs_ph(name):
return U.BatchInput(env.observation_space.shape, name=name)
num_action_grains = num_actions_pad - 1
num_action_dims = env.action_space.shape[0]
num_action_streams = num_action_dims
num_actions = num_actions_pad * num_action_streams # total numb network outputs for action branching with one action dimension per branch
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=num_actions,
num_action_streams=num_action_streams,
batch_size=batch_size,
learning_rate=lr,
grad_norm_clipping=grad_norm_clipping,
gamma=gamma,
scope="deepq",
reuse=None)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': num_actions,
'num_action_streams': num_action_streams,
}
# prioritized_replay: create the replay buffer
replay_buffer = PrioritizedReplayBuffer(buffer_size,
alpha=prioritized_replay_alpha)
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
# epsilon_greedy = False: just greedy policy
exploration = ConstantSchedule(value=0.0) # greedy policy
std_schedule = LinearSchedule(schedule_timesteps=timesteps_std,
initial_p=initial_std,
final_p=final_std)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
# Initialize the parameters used for converting branching, discrete action indeces to continuous actions
low = env.action_space.low
high = env.action_space.high
actions_range = np.subtract(high, low)
episode_rewards = []
reward_sum = 0.0
num_episodes = 0
time_steps = [0]
time_spent_exploring = [0]
prev_time = time.time()
n_trainings = 0
# Set up on-demand rendering of Gym environments using keyboard controls: 'r'ender or 's'top
import termios, fcntl, sys
fd = sys.stdin.fileno()
oldterm = termios.tcgetattr(fd)
newattr = termios.tcgetattr(fd)
newattr[3] = newattr[3] & ~termios.ICANON & ~termios.ECHO
render = False
displayed_mean_reward = None
def evaluate(step, episode_number):
global max_eval_reward_mean, model_saved
print('Evaluate...')
eval_reward_sum = 0.0
# Run evaluation episodes
for eval_episode in range(n_eval_episodes):
obs = env.reset()
done = False
while not done:
# Choose action
action_idxes = np.array(
act(np.array(obs)[None],
stochastic=False)) # deterministic
actions_greedy = action_idxes / num_action_grains * actions_range + low
if eval_std == 0.0:
action = actions_greedy
else:
action = []
for index in range(len(actions_greedy)):
a_greedy = actions_greedy[index]
out_of_range_action = True
while out_of_range_action:
a_stoch = np.random.normal(loc=a_greedy,
scale=eval_std)
a_idx_stoch = np.rint(
(a_stoch + high[index]) /
actions_range[index] * num_action_grains)
if a_idx_stoch >= 0 and a_idx_stoch < num_actions_pad:
action.append(a_stoch)
out_of_range_action = False
# Step
obs, rew, done, _ = env.step(action)
eval_reward_sum += rew
# Average the rewards and log
eval_reward_mean = eval_reward_sum / n_eval_episodes
print(eval_reward_mean, 'over', n_eval_episodes, 'episodes')
with open("results/{}_{}_eval.csv".format(time_stamp, env_name),
"a") as eval_fw:
eval_writer = csv.writer(
eval_fw,
delimiter="\t",
lineterminator="\n",
)
eval_writer.writerow([episode_number, step, eval_reward_mean])
if max_eval_reward_mean is None or eval_reward_mean > max_eval_reward_mean:
logger.log(
"Saving model due to mean eval increase: {} -> {}".format(
max_eval_reward_mean, eval_reward_mean))
U.save_state(model_file)
model_saved = True
max_eval_reward_mean = eval_reward_mean
with tempfile.TemporaryDirectory() as td:
model_file = os.path.join(td, "model")
evaluate(0, 0)
obs = env.reset()
with open("results/{}_{}.csv".format(time_stamp, env_name), "w") as fw:
writer = csv.writer(
fw,
delimiter="\t",
lineterminator="\n",
)
t = -1
while True:
t += 1
# Select action and update exploration probability
action_idxes = np.array(
act(np.array(obs)[None], update_eps=exploration.value(t)))
# Convert sub-actions indexes (discrete sub-actions) to continuous controls
action = action_idxes / num_action_grains * actions_range + low
# epsilon_greedy = False: use Gaussian noise
actions_greedy = action
action_idx_stoch = []
action = []
for index in range(len(actions_greedy)):
a_greedy = actions_greedy[index]
out_of_range_action = True
while out_of_range_action:
# Sample from a Gaussian with mean at the greedy action and a std following a schedule of choice
a_stoch = np.random.normal(loc=a_greedy,
scale=std_schedule.value(t))
# Convert sampled cont action to an action idx
a_idx_stoch = np.rint(
(a_stoch + high[index]) / actions_range[index] *
num_action_grains)
# Check if action is in range
if a_idx_stoch >= 0 and a_idx_stoch < num_actions_pad:
action_idx_stoch.append(a_idx_stoch)
action.append(a_stoch)
out_of_range_action = False
action_idxes = action_idx_stoch
new_obs, rew, done, _ = env.step(action)
# On-demand rendering
if (t + 1) % 100 == 0:
# TO DO better?
termios.tcsetattr(fd, termios.TCSANOW, newattr)
oldflags = fcntl.fcntl(fd, fcntl.F_GETFL)
fcntl.fcntl(fd, fcntl.F_SETFL, oldflags | os.O_NONBLOCK)
try:
try:
c = sys.stdin.read(1)
if c == 'r':
print()
print('Rendering begins...')
render = True
elif c == 's':
print()
print('Stop rendering!')
render = False
env.render(close=True)
except IOError:
pass
finally:
termios.tcsetattr(fd, termios.TCSAFLUSH, oldterm)
fcntl.fcntl(fd, fcntl.F_SETFL, oldflags)
# Visualize Gym environment on render
if render: env.render()
# Store transition in the replay buffer
replay_buffer.add(obs, action_idxes, rew, new_obs, float(done))
obs = new_obs
reward_sum += rew
if done:
obs = env.reset()
time_spent_exploring[-1] = int(100 * exploration.value(t))
time_spent_exploring.append(0)
episode_rewards.append(reward_sum)
time_steps[-1] = t
reward_sum = 0.0
time_steps.append(0)
# Frequently log to file
writer.writerow(
[len(episode_rewards), t, episode_rewards[-1]])
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer
# prioritized_replay
experience = replay_buffer.sample(
batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
td_errors = train(
obses_t, actions, rewards, obses_tp1, dones,
weights) #np.ones_like(rewards)) #TEMP AT NEW
# prioritized_replay
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes,
new_priorities)
n_trainings += 1
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically
update_target()
if len(episode_rewards) == 0: mean_100ep_reward = 0
elif len(episode_rewards) < 100:
mean_100ep_reward = np.mean(episode_rewards)
else:
mean_100ep_reward = np.mean(episode_rewards[-100:])
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)))
current_time = time.time()
logger.record_tabular(
"trainings per second",
n_trainings / (current_time - prev_time))
logger.dump_tabular()
n_trainings = 0
prev_time = current_time
if t > learning_starts and num_episodes > 100:
if displayed_mean_reward is None or mean_100ep_reward > displayed_mean_reward:
if print_freq is not None:
logger.log("Mean reward increase: {} -> {}".format(
displayed_mean_reward, mean_100ep_reward))
displayed_mean_reward = mean_100ep_reward
# Performance evaluation with a greedy policy
if done and num_episodes % eval_freq == 0:
evaluate(t + 1, num_episodes)
obs = env.reset()
# STOP training
if num_episodes >= total_num_episodes:
break
if model_saved:
logger.log("Restore model with mean eval: {}".format(
max_eval_reward_mean))
U.load_state(model_file)
data_to_log = {
'time_steps': time_steps,
'episode_rewards': episode_rewards,
'time_spent_exploring': time_spent_exploring
}
# Write to file the episodic rewards, number of steps, and the time spent exploring
with open("results/{}_{}.txt".format(time_stamp, env_name), 'wb') as fp:
pickle.dump(data_to_log, fp)
return ActWrapper(act, act_params)
def learn_att(env,
q_func,
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
):
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
# q_func = build_q_func(network, **network_kwargs) since no network setting
# 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_att(
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,
#add a mask function for the choice of actions
mask_func=
)
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 main():
with open('cartpole.json', encoding='utf-8') as config_file:
config = json.load(config_file)
env = gym.make('CartPole-v0')
state_shape = env.observation_space.shape
action_count = env.action_space.n
layers = []
for layer in config['layers']:
layers.append(Dense(layer, activation=C.relu))
layers.append(Dense((action_count, config['n']), activation=None))
model_func = Sequential(layers)
replay_buffer = ReplayBuffer(config['buffer_capacity'])
# Fill the buffer with randomly generated samples
state = env.reset()
for i in range(config['buffer_capacity']):
action = env.action_space.sample()
post_state, reward, done, _ = env.step(action)
replay_buffer.add(state.astype(np.float32), action, reward, post_state.astype(np.float32), float(done))
if done:
state = env.reset()
reward_buffer = np.zeros(config['max_episodes'], dtype=np.float32)
losses = []
epsilon_schedule = LinearSchedule(1, 0.01, config['max_episodes'])
agent = CategoricalAgent(state_shape, action_count, model_func, config['vmin'], config['vmax'], config['n'],
lr=config['lr'], gamma=config['gamma'])
log_freq = config['log_freq']
for episode in range(1, config['max_episodes'] + 1):
state = env.reset().astype(np.float32)
done = False
while not done:
action = agent.act(state, epsilon_schedule.value(episode))
post_state, reward, done, _ = env.step(action)
post_state = post_state.astype(np.float32)
replay_buffer.add(state, action, reward, post_state, float(done))
reward_buffer[episode - 1] += reward
state = post_state
minibatch = replay_buffer.sample(config['minibatch_size'])
agent.train(*minibatch)
loss = agent.trainer.previous_minibatch_loss_average
losses.append(loss)
if episode % config['target_update_freq'] == 0:
agent.update_target()
if episode % log_freq == 0:
average = np.sum(reward_buffer[episode - log_freq: episode]) / log_freq
print('Episode {:4d} | Loss: {:6.4f} | Reward: {}'.format(episode, loss, average))
agent.model.save('cartpole.cdqn')
sns.set_style('dark')
pd.Series(reward_buffer).rolling(window=log_freq).mean().plot()
plt.xlabel('Episode')
plt.ylabel('Reward')
plt.title('CartPole - Reward with Time')
plt.show()
plt.plot(np.arange(len(losses)), losses)
plt.xlabel('Episode')
plt.ylabel('Loss')
plt.title('CartPole - Loss with Time')
plt.show()
class Agent(tf.Module):
def __init__(self, config, env):
self.config = config
self.agent_ids = [a for a in range(config.num_agents)]
self.env = env
self.optimizer = tf.keras.optimizers.Adam(self.config.lr)
self.replay_memory, self.beta_schedule = init_replay_memory(config)
self.model = init_network(config)
self.target_model = init_network(config)
self.model.summary()
tf.keras.utils.plot_model(self.model, to_file='./model.png')
if self.config.dueling:
self.agent_heads = self.build_agent_heads_dueling()
self.target_agent_heads = self.build_agent_heads_dueling()
self.agent_heads[0].summary()
tf.keras.utils.plot_model(self.agent_heads[0], to_file='./agent_heads_model.png')
else:
self.agent_heads = self.build_agent_heads()
self.target_agent_heads = self.build_agent_heads()
# Create the schedule for exploration starting from 1.
self.exploration = LinearSchedule(schedule_timesteps=int(config.exploration_fraction * config.num_timesteps),
initial_p=1.0, final_p=config.exploration_final_eps)
if config.load_path is not None:
self.load_models(config.load_path)
self.loss = self.nstep_loss
self.eps = tf.Variable(0.0)
self.one_hot_agents = tf.expand_dims(tf.one_hot(self.agent_ids, len(self.agent_ids), dtype=tf.float32), axis=1)
print(f'self.onehot_agent.shape is {self.one_hot_agents.shape}')
def build_agent_heads(self):
"""
:return: list of heads for agents
- gets tensorflow model and adds heads for each agent
"""
input_shape = self.model.output_shape
heads = []
for a in self.agent_ids:
name = 'head_agent_' + str(a)
head_a = tf.keras.layers.Dense(units=self.config.num_actions, activation=None,
kernel_initializer=tf.keras.initializers.Orthogonal(1.0),
bias_initializer=tf.keras.initializers.Constant(0.0),
name=name)
head_a.build(input_shape)
heads.append(head_a)
return heads
def build_agent_heads_dueling(self):
"""
:return: list of heads for agents
- gets tensorflow model and adds heads for each agent
"""
input_shape = self.model.output_shape[-1]
print(input_shape)
heads = []
inputs = tf.keras.layers.Input(input_shape)
for a in self.agent_ids:
name = 'head_agent_' + str(a)
with tf.name_scope(f'action_value_{name}'):
action_head_a = tf.keras.layers.Dense(units=self.config.num_actions, activation=None,
kernel_initializer=tf.keras.initializers.Orthogonal(1.0),
bias_initializer=tf.keras.initializers.Constant(0.0),
name='action_' + name)(inputs)
with tf.name_scope(f'state_value_{name}'):
state_head_a = tf.keras.layers.Dense(units=self.config.num_actions, activation=None,
kernel_initializer=tf.keras.initializers.Orthogonal(1.0),
bias_initializer=tf.keras.initializers.Constant(0.0),
name='state_' + name)(inputs)
action_scores_mean = tf.reduce_mean(action_head_a, 1)
action_scores_centered = action_head_a - tf.expand_dims(action_scores_mean, 1)
head_a = state_head_a + action_scores_centered
head_a = tf.keras.Model(inputs=inputs, outputs=head_a)
heads.append(head_a)
return heads
@tf.function
def choose_action(self, obs, stochastic=True, update_eps=-1):
"""
:param obs: list observations one for each agent
:param stochastic: True for Train phase and False for test phase
:param update_eps: epsilon update for eps-greedy
:return: actions: list of actions chosen by agents based on observation one for each agent
"""
actions = []
for a in self.agent_ids:
inputs = {0: np.expand_dims(obs[a], 0), 1: self.one_hot_agents[a]}
fc_values = self.model(inputs)
# print(f'fc_values.shape {fc_values.shape}')
q_values = self.agent_heads[a](fc_values)
# print(f'q_values.shape {q_values.shape}')
deterministic_actions = tf.argmax(q_values, axis=1)
# print(f'deterministic_actions {deterministic_actions}')
batch_size = 1
random_actions = tf.random.uniform(tf.stack([batch_size]), minval=0,
maxval=self.config.num_actions, dtype=tf.int64)
# print(f'random_actions {random_actions}')
chose_random = tf.random.uniform(tf.stack([batch_size]), minval=0,
maxval=1, dtype=tf.float32) < self.eps
# print(f'chose_random {chose_random}')
stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)
# print(f'stochastic_actions {stochastic_actions}')
if stochastic:
actions.append(stochastic_actions.numpy()[0])
else:
actions.append(deterministic_actions.numpy()[0])
if update_eps >= 0:
self.eps.assign(update_eps)
# print(f'actions {actions}')
return actions
@tf.function
def value(self, obs):
"""
:param obs: list observations one for each agent
:return: best values based on Q-Learning formula max Q(s',a')
"""
values = []
for a in self.agent_ids:
inputs = {0: np.expand_dims(obs[a], 0), 1: self.one_hot_agents[a]}
fc_values = self.target_model(inputs)
q_values = self.target_agent_heads[a](fc_values)
if self.config.double_q:
fc_values_using_online_net = self.model(inputs)
q_values_using_online_net = self.agent_heads[a](fc_values_using_online_net)
q_value_best_using_online_net = tf.argmax(q_values_using_online_net, 1)
q_tp1_best = tf.reduce_sum(
q_values * tf.one_hot(q_value_best_using_online_net, self.config.num_actions, dtype=tf.float32), 1)
else:
q_tp1_best = tf.reduce_max(q_values, 1)
values.append(q_tp1_best.numpy()[0])
return values
@tf.function()
def nstep_loss(self, obses_t, actions, rewards, weights, agent_id):
# print(f'obses_t.shape {obses_t.shape}')
s = obses_t.shape
obses_t = tf.reshape(obses_t, (s[0]*s[1], *s[2:]))
# print(f'obses_t.shape {obses_t.shape}')
s = actions.shape
actions = tf.reshape(actions, (s[0] * s[1], *s[2:]))
# print(f'actions.shape {actions.shape}')
s = rewards.shape
rewards = tf.reshape(rewards, (s[0] * s[1], *s[2:]))
# print(f'rewards.shape {rewards.shape}')
s = weights.shape
weights = tf.reshape(weights, (s[0] * s[1], *s[2:]))
# print(f'weights.shape {weights.shape}')
inputs = {0: obses_t, 1: tf.tile(self.one_hot_agents[agent_id], (s[0]*s[1], 1))}
fc_values = self.model(inputs)
q_t = self.agent_heads[agent_id](fc_values)
q_t_selected = tf.reduce_sum(q_t * tf.one_hot(actions, self.config.num_actions, dtype=tf.float32), 1)
# print(f'q_t_selected.shape is {q_t_selected.shape}')
td_error = q_t_selected - tf.stop_gradient(rewards)
errors = huber_loss(td_error)
weighted_loss = tf.reduce_mean(weights * errors)
return weighted_loss, td_error
@tf.function()
def train(self, obses_t, actions, rewards, weights):
# print(f'obses_t.shape {obses_t.shape}')
td_errors = []
loss = []
with tf.GradientTape() as tape:
for a in self.agent_ids:
loss_a, td_error = self.loss(obses_t[:, a], actions[:, a], rewards[:, a], weights[:, a], a)
loss.append(loss_a)
td_errors.append(td_error)
sum_loss = tf.reduce_sum(loss)
sum_td_error = tf.reduce_sum(td_error)
print(f'sum_loss is {sum_loss}, loss is {loss}')
param = self.model.trainable_variables
for a in self.agent_ids:
param += self.agent_heads[a].trainable_variables
# print(f'param {param}')
grads = tape.gradient(sum_loss, param)
if self.config.grad_norm_clipping:
clipped_grads = []
for grad in grads:
clipped_grads.append(tf.clip_by_norm(grad, self.config.grad_norm_clipping))
grads = clipped_grads
grads_and_vars = list(zip(grads, param))
self.optimizer.apply_gradients(grads_and_vars)
return sum_loss.numpy(), sum_td_error.numpy()
@tf.function(autograph=False)
def update_target(self):
for var, var_target in zip(self.model.trainable_variables, self.target_model.trainable_variables):
var_target.assign(var)
vars, target_vars = [], []
for a in self.agent_ids:
vars.extend(self.agent_heads[a].trainable_variables)
target_vars.extend(self.target_agent_heads[a].trainable_variables)
for var, var_target in zip(vars, target_vars):
var_target.assign(var)
@tf.function(autograph=False)
def soft_update_target(self):
for var, var_target in zip(self.model.trainable_variables, self.target_model.trainable_variables):
var_target.assign(self.tau * var + (1.0 - self.tau) * var_target)
vars, target_vars = [], []
for a in self.agent_ids:
vars.extend(self.agent_heads[a].trainable_variables)
target_vars.extend(self.target_agent_heads[a].trainable_variables)
for var, var_target in zip(vars, target_vars):
var_target.assign(self.tau * var + (1.0 - self.tau) * var_target)
def save(self, save_path):
self.model.save_weights(f'{save_path}/value_network.h5')
self.target_model.save_weights(f'{save_path}/target_network.h5')
for a in self.agent_ids:
self.agent_heads[a].save_weights(f'{save_path}/agent_{a}_head.h5')
self.target_agent_heads[a].save_weights(f'{save_path}/target_agent_{a}_head.h5')
def load(self, load_path):
self.model.load_weights(f'{load_path}/value_network.h5')
self.target_model.load_weights(f'{load_path}/target_network.h5')
for a in self.agent_ids:
self.agent_heads[a].load_weights(f'{load_path}/agent_{a}_head.h5')
self.target_agent_heads[a].load_weights(f'{load_path}/target_agent_{a}_head.h5')
def learn(self):
episode_rewards = [0.0]
saved_mean_reward = None
obs = self.env.reset()
done = False
# Start total timer
tstart = time.time()
for t in range(self.config.num_timesteps):
update_eps = tf.constant(self.exploration.value(t))
if t % self.config.print_freq == 0:
time_1000_step = time.time()
nseconds = time_1000_step - tstart
tstart = time_1000_step
print(f'time spend to perform {t - self.config.print_freq} to {t} steps is {nseconds} ')
print('eps update', self.exploration.value(t))
mb_obs, mb_rewards, mb_actions, mb_values, mb_dones = [], [], [], [], []
# mb_states = states
epinfos = []
for _ in range(self.config.n_steps):
actions = self.choose_action(tf.constant(obs), update_eps=update_eps)
# print(f'actions is {actions}')
mb_obs.append(obs.copy())
mb_actions.append(actions)
mb_dones.append([float(done) for _ in self.agent_ids])
obs1, rews, done, info = self.env.step(actions)
if self.config.same_reward_for_agents:
rews = [np.max(rews) for _ in range(len(rews))] # for cooperative purpose same reward for every one
mb_rewards.append(rews)
obs = obs1
maybeepinfo = info.get('episode')
if maybeepinfo : epinfos.append(maybeepinfo)
episode_rewards[-1] += np.max(rews)
if done:
episode_rewards.append(0.0)
obs = self.env.reset()
mb_dones.append([float(done) for _ in self.agent_ids])
# swap axes to have lists in shape of (num_agents, num_steps, ...)
mb_obs = np.asarray(mb_obs, dtype=obs[0].dtype).swapaxes(0, 1)
mb_actions = np.asarray(mb_actions, dtype=actions[0].dtype).swapaxes(0, 1)
mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(0, 1)
mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(0, 1)
mb_masks = mb_dones[:, -1]
mb_dones = mb_dones[:, 1:]
# print(f'before discount mb_rewards is {mb_rewards}')
if self.config.gamma > 0.0:
# Discount/bootstrap off value fn
last_values = self.value(tf.constant(obs1))
# print(f'last_values {last_values}')
for n, (rewards, dones, value) in enumerate(zip(mb_rewards, mb_dones, last_values)):
rewards = rewards.tolist()
dones = dones.tolist()
if dones[-1] == 0:
rewards = discount_with_dones(rewards + [value], dones + [0], self.config.gamma)[:-1]
else:
rewards = discount_with_dones(rewards, dones, self.config.gamma)
mb_rewards[n] = rewards
# print(f'after discount mb_rewards is {mb_rewards}')
if self.config.replay_buffer is not None:
self.replay_memory.add((mb_obs, mb_actions, mb_rewards, obs1, mb_masks))
if t > self.config.learning_starts and t % self.config.train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if self.config.prioritized_replay:
experience = self.replay_memory.sample(self.config.batch_size, beta=self.beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights, batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = self.replay_memory.sample(self.config.batch_size)
weights, batch_idxes = np.ones_like(rewards), None
obses_t = tf.constant(obses_t)
actions = tf.constant(actions)
rewards = tf.constant(rewards)
weights = tf.constant(weights)
loss, td_errors = self.train(obses_t, actions, rewards, weights)
if t > self.config.learning_starts and t % self.config.target_network_update_freq == 0:
# Update target network periodically.
self.soft_update_target()
if t % self.config.playing_test == 0 and t != 0:
self.play_test_games()
mean_100ep_reward = np.mean(episode_rewards[-101:-1])
num_episodes = len(episode_rewards)
if done and self.config.print_freq is not None and len(episode_rewards) % self.config.print_freq == 0:
print(f'last 100 episode mean reward {mean_100ep_reward} in {num_episodes} playing')
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 * self.exploration.value(t)))
logger.dump_tabular()
def play_test_games(self):
num_tests = self.config.num_tests
test_env = init_env(self.config, mode='test')
test_rewards = np.zeros(num_tests)
for i in range(num_tests):
test_done = False
test_obs_all = test_env.reset()
# print(np.asarray(test_obs_all).shape)
while not test_done:
test_obs_all = tf.constant(test_obs_all)
test_action_list = self.choose_action(test_obs_all, stochastic=False)
test_new_obs_list, test_rew_list, test_done, _ = test_env.step(test_action_list)
test_obs_all = test_new_obs_list
if test_done:
print(f'test_reward_dict for test {i} is {test_rew_list}')
test_rewards[i] = np.mean(test_rew_list)
print(f'mean reward of {num_tests} tests is {np.mean(test_rewards)}')
test_env.close()
