class DeepActorCriticAgent(mp.Process):
def __init__(self, id, env_name, agent_params, env_params):
"""
An Advantage Actor-Critic Agent that uses a Deep Neural Network to represent it's Policy and the Value function
:param id: An integer ID to identify the agent in case there are multiple agent instances
:param env_name: Name/ID of the environment
:param agent_params: Parameters to be used by the agent
"""
super(DeepActorCriticAgent, self).__init__()
self.id = id
self.actor_name = "actor" + str(self.id)
self.env_name = env_name
self.params = agent_params
self.env_conf = env_params
self.policy = self.multi_variate_gaussian_policy
self.gamma = self.params['gamma']
self.trajectory = [
] # Contains the trajectory of the agent as a sequence of Transitions
self.rewards = [
] # Contains the rewards obtained from the env at every step
self.global_step_num = 0
self.best_mean_reward = -float(
"inf") # Agent's personal best mean episode reward
self.best_reward = -float("inf")
self.saved_params = False # Whether or not the params have been saved along with the model to model_dir
self.continuous_action_space = True #Assumption by default unless env.action_space is Discrete
def multi_variate_gaussian_policy(self, obs):
"""
Calculates a multi-variate gaussian distribution over actions given observations
:param obs: Agent's observation
:return: policy, a distribution over actions for the given observation
"""
mu, sigma = self.actor(obs)
value = self.critic(obs)
[
mu[:, i].clamp_(float(self.env.action_space.low[i]),
float(self.env.action_space.high[i]))
for i in range(self.action_shape)
] # Clamp each dim of mu based on the (low,high) limits of that action dim
sigma = torch.nn.Softplus()(
sigma).squeeze() + 1e-7 # Let sigma be (smoothly) +ve
self.mu = mu.to(torch.device("cpu"))
self.sigma = sigma.to(torch.device("cpu"))
self.value = value.to(torch.device("cpu"))
if len(self.mu.shape) == 0: # See if mu is a scalar
#self.mu = self.mu.unsqueeze(0) # This prevents MultivariateNormal from crashing with SIGFPE
self.mu.unsqueeze_(0)
self.action_distribution = MultivariateNormal(
self.mu,
torch.eye(self.action_shape) * self.sigma,
validate_args=True)
return self.action_distribution
def discrete_policy(self, obs):
"""
Calculates a discrete/categorical distribution over actions given observations
:param obs: Agent's observation
:return: policy, a distribution over actions for the given observation
"""
logits = self.actor(obs)
value = self.critic(obs)
self.logits = logits.to(torch.device("cpu"))
self.value = value.to(torch.device("cpu"))
self.action_distribution = Categorical(logits=self.logits)
return self.action_distribution
def preproc_obs(self, obs):
obs = np.array(
obs
) # Obs could be lazy frames. So, force fetch before moving forward
if len(obs.shape) == 3:
# Reshape obs from (H x W x C) order to this order: C x W x H and resize to (C x 84 x 84)
obs = np.reshape(obs, (obs.shape[2], obs.shape[1], obs.shape[0]))
obs = np.resize(obs, (obs.shape[0], 84, 84))
# Convert to torch Tensor, add a batch dimension, convert to float repr
obs = torch.from_numpy(obs).unsqueeze(0).float()
return obs
def process_action(self, action):
if self.continuous_action_space:
[
action[:, i].clamp_(float(self.env.action_space.low[i]),
float(self.env.action_space.high[i]))
for i in range(self.action_shape)
] # Limit the action to lie between the (low, high) limits of the env
action = action.to(torch.device("cpu"))
return action.numpy().squeeze(
0
) # Convert to numpy ndarray, squeeze and remove the batch dimension
def get_action(self, obs):
obs = self.preproc_obs(obs)
action_distribution = self.policy(
obs
) # Call to self.policy(obs) also populates self.value with V(obs)
value = self.value
action = action_distribution.sample()
log_prob_a = action_distribution.log_prob(action)
action = self.process_action(action)
self.trajectory.append(Transition(
obs, value, action, log_prob_a)) # Construct the trajectory
return action
def calculate_n_step_return(self, n_step_rewards, final_state, done,
gamma):
"""
Calculates the n-step return for each state in the input-trajectory/n_step_transitions
:param n_step_rewards: List of rewards for each step
:param final_state: Final state in this n_step_transition/trajectory
:param done: True rf the final state is a terminal state if not, False
:return: The n-step return for each state in the n_step_transitions
"""
g_t_n_s = list()
with torch.no_grad():
g_t_n = torch.tensor([[0]]).float() if done else self.critic(
self.preproc_obs(final_state)).cpu()
for r_t in n_step_rewards[::
-1]: # Reverse order; From r_tpn to r_t
g_t_n = torch.tensor(r_t).float() + self.gamma * g_t_n
g_t_n_s.insert(
0, g_t_n
) # n-step returns inserted to the left to maintain correct index order
return g_t_n_s
def calculate_loss(self, trajectory, td_targets):
"""
Calculates the critic and actor losses using the td_targets and self.trajectory
:param td_targets:
:return:
"""
n_step_trajectory = Transition(*zip(*trajectory))
v_s_batch = n_step_trajectory.value_s
log_prob_a_batch = n_step_trajectory.log_prob_a
actor_losses, critic_losses = [], []
for td_target, critic_prediction, log_p_a in zip(
td_targets, v_s_batch, log_prob_a_batch):
td_err = td_target - critic_prediction
actor_losses.append(
-log_p_a *
td_err) # td_err is an unbiased estimated of Advantage
critic_losses.append(F.smooth_l1_loss(critic_prediction,
td_target))
#critic_loss.append(F.mse_loss(critic_pred, td_target))
if self.params["use_entropy_bonus"]:
actor_loss = torch.stack(actor_losses).mean(
) - self.action_distribution.entropy().mean()
else:
actor_loss = torch.stack(actor_losses).mean()
critic_loss = torch.stack(critic_losses).mean()
writer.add_scalar(self.actor_name + "/critic_loss", critic_loss,
self.global_step_num)
writer.add_scalar(self.actor_name + "/actor_loss", actor_loss,
self.global_step_num)
return actor_loss, critic_loss
def learn(self, n_th_observation, done):
if self.params["clip_rewards"]:
self.rewards = np.sign(
self.rewards).tolist() # Clip rewards to -1 or 0 or +1
td_targets = self.calculate_n_step_return(self.rewards,
n_th_observation, done,
self.gamma)
actor_loss, critic_loss = self.calculate_loss(self.trajectory,
td_targets)
self.actor_optimizer.zero_grad()
actor_loss.backward(retain_graph=True)
self.actor_optimizer.step()
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
self.trajectory.clear()
self.rewards.clear()
def save(self):
model_file_name = self.params[
"model_dir"] + "A2C_" + self.env_name + ".ptm"
agent_state = {
"Actor": self.actor.state_dict(),
"Critic": self.critic.state_dict(),
"best_mean_reward": self.best_mean_reward,
"best_reward": self.best_reward
}
torch.save(agent_state, model_file_name)
print("Agent's state is saved to", model_file_name)
# Export the params used if not exported already
if not self.saved_params:
params_manager.export_agent_params(model_file_name +
".agent_params")
print("The parameters have been saved to",
model_file_name + ".agent_params")
self.saved_params = True
def load(self):
model_file_name = self.params[
"model_dir"] + "A2C_" + self.env_name + ".ptm"
agent_state = torch.load(model_file_name,
map_location=lambda storage, loc: storage)
self.actor.load_state_dict(agent_state["Actor"])
self.critic.load_state_dict(agent_state["Critic"])
self.actor.to(device)
self.critic.to(device)
self.best_mean_reward = agent_state["best_mean_reward"]
self.best_reward = agent_state["best_reward"]
print("Loaded Advantage Actor-Critic model state from",
model_file_name, " which fetched a best mean reward of:",
self.best_mean_reward, " and an all time best reward of:",
self.best_reward)
def run(self):
# If a custom useful_region configuration for this environment ID is available, use it if not use the Default.
# Currently this is utilized for only the Atari env. Follows the same procedure as in Chapter 6
custom_region_available = False
for key, value in self.env_conf['useful_region'].items():
if key in args.env:
self.env_conf['useful_region'] = value
custom_region_available = True
break
if custom_region_available is not True:
self.env_conf['useful_region'] = self.env_conf['useful_region'][
'Default']
atari_env = False
for game in Atari.get_games_list():
if game in args.env.lower():
atari_env = True
if atari_env: # Use the Atari wrappers (like we did in Chapter 6) if it's an Atari env
self.env = Atari.make_env(self.env_name, self.env_conf)
else:
#print("Given environment name is not an Atari Env. Creating a Gym env")
self.env = gym.make(self.env_name)
self.state_shape = self.env.observation_space.shape
if isinstance(self.env.action_space.sample(),
int): # Discrete action space
self.action_shape = self.env.action_space.n
self.policy = self.discrete_policy
self.continuous_action_space = False
else: # Continuous action space
self.action_shape = self.env.action_space.shape[0]
self.policy = self.multi_variate_gaussian_policy
self.critic_shape = 1
if len(self.state_shape
) == 3: # Screen image is the input to the agent
if self.continuous_action_space:
self.actor = DeepActor(self.state_shape, self.action_shape,
device).to(device)
else: # Discrete action space
self.actor = DeepDiscreteActor(self.state_shape,
self.action_shape,
device).to(device)
self.critic = DeepCritic(self.state_shape, self.critic_shape,
device).to(device)
else: # Input is a (single dimensional) vector
if self.continuous_action_space:
#self.actor_critic = ShallowActorCritic(self.state_shape, self.action_shape, 1, self.params).to(device)
self.actor = ShallowActor(self.state_shape, self.action_shape,
device).to(device)
else: # Discrete action space
self.actor = ShallowDiscreteActor(self.state_shape,
self.action_shape,
device).to(device)
self.critic = ShallowCritic(self.state_shape, self.critic_shape,
device).to(device)
self.actor_optimizer = torch.optim.Adam(
self.actor.parameters(), lr=self.params["learning_rate"])
self.critic_optimizer = torch.optim.Adam(
self.critic.parameters(), lr=self.params["learning_rate"])
# Handle loading and saving of trained Agent models
episode_rewards = list()
prev_checkpoint_mean_ep_rew = self.best_mean_reward
num_improved_episodes_before_checkpoint = 0 # To keep track of the num of ep with higher perf to save model
#print("Using agent_params:", self.params)
if self.params['load_trained_model']:
try:
self.load()
prev_checkpoint_mean_ep_rew = self.best_mean_reward
except FileNotFoundError:
if args.test: # Test a saved model
print(
"FATAL: No saved model found. Cannot test. Press any key to train from scratch"
)
input()
else:
print(
"WARNING: No trained model found for this environment. Training from scratch."
)
for episode in range(self.params["max_num_episodes"]):
obs = self.env.reset()
done = False
ep_reward = 0.0
step_num = 0
while not done:
action = self.get_action(obs)
next_obs, reward, done, _ = self.env.step(action)
self.rewards.append(reward)
ep_reward += reward
step_num += 1
if not args.test and (
step_num >= self.params["learning_step_thresh"]
or done):
self.learn(next_obs, done)
step_num = 0
# Monitor performance and save Agent's state when perf improves
if done:
episode_rewards.append(ep_reward)
if ep_reward > self.best_reward:
self.best_reward = ep_reward
if np.mean(
episode_rewards) > prev_checkpoint_mean_ep_rew:
num_improved_episodes_before_checkpoint += 1
if num_improved_episodes_before_checkpoint >= self.params[
"save_freq_when_perf_improves"]:
prev_checkpoint_mean_ep_rew = np.mean(
episode_rewards)
self.best_mean_reward = np.mean(episode_rewards)
self.save()
num_improved_episodes_before_checkpoint = 0
obs = next_obs
self.global_step_num += 1
if args.render:
self.env.render()
#print(self.actor_name + ":Episode#:", episode, "step#:", step_num, "\t rew=", reward, end="\r")
writer.add_scalar(self.actor_name + "/reward", reward,
self.global_step_num)
print(
"{}:Episode#:{} \t ep_reward:{} \t mean_ep_rew:{}\t best_ep_reward:{}"
.format(self.actor_name, episode, ep_reward,
np.mean(episode_rewards), self.best_reward))
writer.add_scalar(self.actor_name + "/ep_reward", ep_reward,
self.global_step_num)
class DeepActorCriticAgent():
def __init__(self, id, env_names, agent_params):
"""
An Actor-Critic Agent that uses a Deep Neural Network to represent it's Policy and the Value function
:param state_shape:
:param action_shape:
"""
super(DeepActorCriticAgent, self).__init__()
self.id = id
self.actor_name = "actor" + str(self.id)
self.env_names = env_names
self.params = agent_params
self.policy = self.multi_variate_gaussian_policy
self.gamma = self.params['gamma']
self.trajectory = [
] # Contains the trajectory of the agent as a sequence of Transitions
self.rewards = [
] # Contains the rewards obtained from the env at every step
self.global_step_num = 0
self.best_mean_reward = -float(
"inf") # Agent's personal best mean episode reward
self.best_reward = -float("inf")
self.saved_params = False # Whether or not the params have been saved along with the model to model_dir
self.continuous_action_space = True # Assumption by default unless env.action_space is Discrete
def multi_variate_gaussian_policy(self, obs):
"""
Calculates a multi-variate gaussian distribution over actions given observations
:param obs: Agent's observation
:return: policy, a distribution over actions for the given observation
"""
mu, sigma = self.actor(obs)
value = self.critic(obs).squeeze()
[
mu[:, i].clamp_(float(self.envs.action_space.low[i]),
float(self.envs.action_space.high[i]))
for i in range(self.action_shape)
] # Clamp each dim of mu based on the (low,high) limits of that action dim
sigma = torch.nn.Softplus()(
sigma) + 1e-7 # Let sigma be (smoothly) +ve
self.mu = mu.to(torch.device("cpu"))
self.sigma = sigma.to(torch.device("cpu"))
self.value = value.to(torch.device("cpu"))
if len(self.mu[0].shape) == 0: # See if mu is a scalar
self.mu = self.mu.unsqueeze(
0
) # This prevents MultivariateNormal from crashing with SIGFPE
self.covariance = torch.stack(
[torch.eye(self.action_shape) * s for s in self.sigma])
if self.action_shape == 1:
self.covariance = self.sigma.unsqueeze(
-1
) # Make the covariance a square mat to avoid RuntimeError with MultivariateNormal
self.action_distribution = MultivariateNormal(self.mu, self.covariance)
return self.action_distribution
def discrete_policy(self, obs):
"""
Calculates a discrete/categorical distribution over actions given observations
:param obs: Agent's observation
:return: policy, a distribution over actions for the given observation
"""
logits = self.actor(obs)
value = self.critic(obs).squeeze()
self.logits = logits.to(torch.device("cpu"))
self.value = value.to(torch.device("cpu"))
self.action_distribution = Categorical(logits=self.logits)
return self.action_distribution
def preproc_obs(self, obs):
if len(
obs[0].shape
) == 3: # shape of obs:(num_agents, obs_im_height, obs_im_width, obs_num_channels)
# Reshape obs from (B x H x W x C) order to this order: B x C x W x H and resize to (C x 84 x 84)
obs = np.reshape(obs,
(-1, obs.shape[3], obs.shape[2], obs.shape[1]))
# The environment wrapper already takes care of reshaping image obs into 84 x 84 x C. Can be skipped
obs = np.resize(obs, (-1, obs.shape[1], 84, 84))
# Convert to torch Tensor, convert to float repr
obs = torch.from_numpy(obs).float()
return obs
def process_action(self, action):
if self.continuous_action_space:
[
action[:, i].clamp_(float(self.envs.action_space.low[i]),
float(self.envs.action_space.high[i]))
for i in range(self.action_shape)
] # Limit the action to lie between the (low, high) limits of the env
action = action.to(torch.device("cpu"))
return action.numpy()
def get_action(self, obs):
obs = self.preproc_obs(obs)
action_distributions = self.policy(
obs
) # Call to self.policy(obs) also populates self.value with V(obs)
value = self.value
actions = action_distributions.sample()
log_prob_a = action_distributions.log_prob(actions)
actions = self.process_action(actions)
# Store the n-step trajectory for learning. Skip storing the trajectory in test only mode
if not self.params["test"]:
self.trajectory.append(Transition(
obs, value, actions, log_prob_a)) # Construct the trajectory
return actions
# TODO: rename num_agents to num_actors in parameters.json file to be consistent with comments
def calculate_n_step_return(self, n_step_rewards, next_states, dones,
gamma):
"""
Calculates the n-step return for each state in the input-trajectory/n_step_transitions for the "done" actors
:param n_step_rewards: List of length=num_steps containing rewards of shape=(num_actors x 1)
:param next_states: list of length=num_actors containing next observations of shape=(obs_shape)
:param dones: list of length=num_actors containing True if the next_state is a terminal state if not, False
:return: The n-step return for each state in the n_step_transitions
"""
g_t_n_s = list()
with torch.no_grad():
# 1. Calculate next-state values for each actor:
# a. If next_state is terminal (done[actor_idx]=True), set g_t_n[actor_idx]=0
# b. If next_state is non-terminal (done[actor_idx]=False), set g_t_n[actor_idx] to Critic's prediction
g_t_n = torch.tensor([[not d] for d in dones]).float() # 1. a.
# See if there is at least one non-terminal next-state
if np.where([not d for d in dones])[0].size > 0:
non_terminal_idxs = torch.tensor(
np.where([not d for d in dones])).squeeze(0)
g_t_n[non_terminal_idxs] = self.critic(
self.preproc_obs(
next_states[non_terminal_idxs])).cpu() # 1. b.
g_t_n_s_batch = []
n_step_rewards = torch.stack(
n_step_rewards) # tensor of shape (num_steps x num_actors x 1)
# For each actor
for actor_idx in range(n_step_rewards.shape[1]):
actor_n_step_rewards = n_step_rewards.index_select(
1, torch.tensor([actor_idx])) # shape:(num_steps,1)
g_t_n_s = []
# Calculate n number of n-step returns
for r_t in actor_n_step_rewards.numpy(
)[::
-1]: # Reverse order; From r_tpn to r_t; PyTorch can't slice in reverse #229
g_t_n[actor_idx] = torch.tensor(
r_t).float() + self.gamma * g_t_n[actor_idx]
g_t_n_s.insert(
0, g_t_n[actor_idx].clone()
) # n-step returns inserted to the left to maintain correct index order
g_t_n_s_batch.append(g_t_n_s)
return torch.tensor(
g_t_n_s_batch) # tensor of shape:(num_actors, num_steps, 1)
def calculate_loss(self, trajectory, td_targets):
"""
Calculates the critic and actor losses using the td_targets and self.trajectory
:param trajectory: List of trajectories from all the actors
:param td_targets: Tensor of shape:(num_actors, num_steps, 1)
:return:
"""
n_step_trajectory = Transition(*zip(*trajectory))
# n_step_trajectory.x returns a list of length= num_steps containing num_actors x shape_of_x items
# 1. Create tensor of shape:(num_steps x num_actors x shape_of_x) (using torch.stack())
# 2. Reshape the tensor to be of shape:(num_actors x num_steps x shape_of_x) (using torch.transpose(1,0)
v_s_batch = torch.stack(n_step_trajectory.value_s).transpose(
1, 0) # shape:(num_actors, num_steps, 1)
log_prob_a_batch = torch.stack(n_step_trajectory.log_prob_a).transpose(
1, 0) # shape:(num_actors, num_steps, 1)
actor_losses, critic_losses = [], []
for td_targets, critic_predictions, log_p_a in zip(
td_targets, v_s_batch, log_prob_a_batch):
td_err = td_targets - critic_predictions
actor_losses.append(
-log_p_a *
td_err) # td_err is an unbiased estimated of Advantage
critic_losses.append(
F.smooth_l1_loss(critic_predictions, td_targets))
#critic_loss.append(F.mse_loss(critic_pred, td_target))
if self.params["use_entropy_bonus"]:
actor_loss = torch.stack(actor_losses).mean(
) - self.action_distribution.entropy().mean()
else:
actor_loss = torch.stack(actor_losses).mean()
critic_loss = torch.stack(critic_losses).mean()
writer.add_scalar(self.actor_name + "/critic_loss", critic_loss,
self.global_step_num)
writer.add_scalar(self.actor_name + "/actor_loss", actor_loss,
self.global_step_num)
return actor_loss, critic_loss
def learn(self, n_th_observations, dones):
td_targets = self.calculate_n_step_return(self.rewards,
n_th_observations, dones,
self.gamma)
actor_loss, critic_loss = self.calculate_loss(self.trajectory,
td_targets)
self.actor_optimizer.zero_grad()
actor_loss.backward(retain_graph=True)
self.actor_optimizer.step()
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
self.trajectory.clear()
self.rewards.clear()
def save(self):
model_file_name = self.params[
"model_dir"] + "Batch-A2C_" + self.env_names[0] + ".ptm"
agent_state = {
"Actor": self.actor.state_dict(),
"Critic": self.critic.state_dict(),
"best_mean_reward": self.best_mean_reward,
"best_reward": self.best_reward
}
torch.save(agent_state, model_file_name)
print("Agent's state is saved to", model_file_name)
# Export the params used if not exported already
if not self.saved_params:
params_manager.export_agent_params(model_file_name +
".agent_params")
print("The parameters have been saved to",
model_file_name + ".agent_params")
self.saved_params = True
def load(self):
model_file_name = self.params[
"model_dir"] + "Batch-A2C_" + self.env_names[0] + ".ptm"
agent_state = torch.load(model_file_name,
map_location=lambda storage, loc: storage)
self.actor.load_state_dict(agent_state["Actor"])
self.critic.load_state_dict(agent_state["Critic"])
self.actor.to(device)
self.critic.to(device)
self.best_mean_reward = agent_state["best_mean_reward"]
self.best_reward = agent_state["best_reward"]
print("Loaded Advantage Actor-Critic model state from",
model_file_name, " which fetched a best mean reward of:",
self.best_mean_reward, " and an all time best reward of:",
self.best_reward)
def run(self):
self.envs = SubprocVecEnv(self.env_names)
self.state_shape = self.envs.observation_space.shape
if isinstance(self.envs.action_space.sample(),
int): # Discrete action space
self.action_shape = self.envs.action_space.n
self.policy = self.discrete_policy
self.continuous_action_space = False
else: # Continuous action space
self.action_shape = self.envs.action_space.shape[0]
self.policy = self.multi_variate_gaussian_policy
self.critic_shape = 1
if len(self.state_shape
) == 3: # Screen image is the input to the agent
if self.continuous_action_space:
self.actor = DeepActor(self.state_shape, self.action_shape,
device).to(device)
else: # Discrete action space
self.actor = DeepDiscreteActor(self.state_shape,
self.action_shape,
device).to(device)
self.critic = DeepCritic(self.state_shape, self.critic_shape,
device).to(device)
else: # Input is a (single dimensional) vector
if self.continuous_action_space:
#self.actor_critic = ShallowActorCritic(self.state_shape, self.action_shape, 1, self.params).to(device)
self.actor = ShallowActor(self.state_shape, self.action_shape,
device).to(device)
else: # Discrete action space
self.actor = ShallowDiscreteActor(self.state_shape,
self.action_shape,
device).to(device)
self.critic = ShallowCritic(self.state_shape, self.critic_shape,
device).to(device)
self.actor_optimizer = torch.optim.Adam(
self.actor.parameters(), lr=self.params["learning_rate"])
self.critic_optimizer = torch.optim.Adam(
self.critic.parameters(), lr=self.params["learning_rate"])
# Handle loading and saving of trained Agent models
episode_rewards = list()
prev_checkpoint_mean_ep_rew = self.best_mean_reward
num_improved_episodes_before_checkpoint = 0 # To keep track of the num of ep with higher perf to save model
#print("Using agent_params:", self.params)
if self.params['load_trained_model']:
try:
self.load()
prev_checkpoint_mean_ep_rew = self.best_mean_reward
except FileNotFoundError:
if args.test: # Test a saved model
print(
"FATAL: No saved model found. Cannot test. Press any key to train from scratch"
)
input()
else:
print(
"WARNING: No trained model found for this environment. Training from scratch."
)
#for episode in range(self.params["max_num_episodes"]):
obs = self.envs.reset()
# TODO: Create appropriate masks to take care of envs that have set dones to True & learn() accordingly
episode = 0
cum_step_rewards = np.zeros(self.params["num_agents"])
episode_rewards = []
step_num = 0
while True:
action = self.get_action(obs)
next_obs, rewards, dones, _ = self.envs.step(action)
self.rewards.append(torch.tensor(rewards))
done_env_idxs = np.where(dones)[0]
cum_step_rewards += rewards # nd-array of shape=num_actors
step_num += self.params["num_agents"]
episode += done_env_idxs.size # Update the number of finished episodes
if not args.test and (
step_num >= self.params["learning_step_thresh"]
or done_env_idxs.size):
self.learn(next_obs, dones)
step_num = 0
# Monitor performance and save Agent's state when perf improves
if done_env_idxs.size > 0:
[
episode_rewards.append(r)
for r in cum_step_rewards[done_env_idxs]
]
if np.max(cum_step_rewards[done_env_idxs]
) > self.best_reward:
self.best_reward = np.max(
cum_step_rewards[done_env_idxs])
if np.mean(episode_rewards) > prev_checkpoint_mean_ep_rew:
num_improved_episodes_before_checkpoint += 1
if num_improved_episodes_before_checkpoint >= self.params[
"save_freq_when_perf_improves"]:
prev_checkpoint_mean_ep_rew = np.mean(episode_rewards)
self.best_mean_reward = np.mean(episode_rewards)
self.save()
num_improved_episodes_before_checkpoint = 0
writer.add_scalar(self.actor_name + "/mean_ep_rew",
np.mean(cum_step_rewards[done_env_idxs]),
self.global_step_num)
# Reset the cum_step_rew for the done envs
cum_step_rewards[done_env_idxs] = 0.0
obs = next_obs
self.global_step_num += self.params["num_agents"]
if args.render:
self.envs.render()
#print(self.actor_name + ":Episode#:", episode, "step#:", step_num, "\t rew=", reward, end="\r")
writer.add_scalar(self.actor_name + "/reward",
np.mean(cum_step_rewards), self.global_step_num)
print(
"{}:Episode#:{} \t avg_step_reward:{:.4} \t mean_ep_rew:{:.4}\t best_ep_reward:{:.4}"
.format(self.actor_name, episode, np.mean(cum_step_rewards),
np.mean(episode_rewards), self.best_reward))