def train(self, batch: EpisodeBatch, t_env: int, episode_num: int):
# Get the relevant quantities
bs = batch.batch_size
max_t = batch.max_seq_length
rewards = batch["reward"][:, :-1]
actions = batch["actions"][:, :]
terminated = batch["terminated"][:, :-1].float()
mask = batch["filled"][:, :-1].float()
mask[:, 1:] = mask[:, 1:] * (1 - terminated[:, :-1])
avail_actions = batch["avail_actions"][:, :-1]
# Calculate action policy distribution and entropy
mac_out = []
mac_out_entropy = []
self.mac.init_hidden(batch.batch_size)
for t in range(batch.max_seq_length - 1):
agent_outs = self.mac.forward(batch, t=t, return_logits=True)
agent_entropy = multinomial_entropy(agent_outs).mean(dim=-1,
keepdim=True)
agent_probs = th.nn.functional.softmax(agent_outs, dim=-1)
mac_out.append(agent_probs)
mac_out_entropy.append(agent_entropy)
mac_out = th.stack(mac_out, dim=1) # Concat over time
mac_out_entropy = th.stack(mac_out_entropy, dim=1)
# Mask out unavailable actions, renormalise (as in action selection)
mac_out[avail_actions == 0] = 0
mac_out = mac_out / mac_out.sum(dim=-1, keepdim=True)
mac_out[avail_actions == 0] = 0
# Mix action probability and state to estimate joint Q-value
mix_loss = self.critic(mac_out, batch["state"][:, :-1])
mask = mask.expand_as(mix_loss)
entropy_mask = copy.deepcopy(mask)
mix_loss = (mix_loss * mask).sum() / mask.sum()
# Adaptive Entropy Regularization
entropy_loss = (mac_out_entropy *
entropy_mask).sum() / entropy_mask.sum()
entropy_ratio = self.entropy_coef / entropy_loss.item()
mix_loss = -mix_loss - entropy_ratio * entropy_loss
# Optimise agents
self.agent_optimiser.zero_grad()
mix_loss.backward()
grad_norm = th.nn.utils.clip_grad_norm_(self.agent_params,
self.args.grad_norm_clip)
self.agent_optimiser.step()
if t_env - self.log_stats_t_agent >= self.args.learner_log_interval:
self.logger.log_stat("mix_loss", mix_loss.item(), t_env)
self.logger.log_stat("entropy", entropy_loss.item(), t_env)
self.logger.log_stat("agent_grad_norm", grad_norm, t_env)
self.log_stats_t_agent = t_env
def train(self, batch: EpisodeBatch, t_env: int, episode_num: int):
self.train_critic_td(batch, t_env, episode_num)
# Get the relevant quantities
bs = batch.batch_size
max_t = batch.max_seq_length
rewards = batch["reward"][:, :-1]
actions = batch["actions"][:, :]
terminated = batch["terminated"][:, :-1].float()
mask = batch["filled"][:, :-1].float()
mask[:, 1:] = mask[:, 1:] * (1 - terminated[:, :-1])
avail_actions = batch["avail_actions"][:, :-1]
mac_out = []
mac_out_entropy = []
self.mac.init_hidden(batch.batch_size)
for t in range(batch.max_seq_length - 1):
# -------------------------------------------------------------------------------------#
# NOTE: We hard-coded the forward pass arguments for experiment, we will fix this later
# -------------------------------------------------------------------------------------#
agent_outs = self.mac.forward(batch, t=t, test_mode=True, gumbel=True)
agent_entropy = multinomial_entropy(agent_outs).mean(dim=-1, keepdim=True)
agent_probs = th.nn.functional.softmax(agent_outs, dim=-1)
mac_out.append(agent_probs)
mac_out_entropy.append(agent_entropy)
mac_out = th.stack(mac_out, dim=1) # Concat over time
mac_out_entropy = th.stack(mac_out_entropy, dim=1)
# Mask out unavailable actions, renormalise (as in action selection)
mac_out[avail_actions == 0] = 0
mac_out = mac_out/mac_out.sum(dim=-1, keepdim=True)
mac_out[avail_actions == 0] = 0
mix_loss = self.critic(mac_out, batch["state"][:, :-1])
mask = mask.expand_as(mix_loss)
entropy_mask = copy.deepcopy(mask)
mix_loss = (mix_loss * mask).sum() / mask.sum()
entropy_loss = (mac_out_entropy * entropy_mask).sum() / entropy_mask.sum()
entropy_ratio = self.entropy_coef / entropy_loss.item()
mix_loss = - mix_loss - entropy_ratio * entropy_loss
# Optimise agents
self.agent_optimiser.zero_grad()
mix_loss.backward()
grad_norm = th.nn.utils.clip_grad_norm_(self.agent_params, self.args.grad_norm_clip)
self.agent_optimiser.step()
if t_env - self.log_stats_t_agent >= self.args.learner_log_interval:
self.logger.log_stat("mix_loss", mix_loss.item(), t_env)
self.logger.log_stat("entropy", entropy_loss.item(), t_env)
self.logger.log_stat("agent_grad_norm", grad_norm, t_env)
self.log_stats_t_agent = t_env