import copy from components.episode_buffer import EpisodeBatch from modules.critics.coma import COMACritic from utils.rl_utils import build_td_lambda_targets import torch as th from torch.optim import RMSprop class COMALearner: def __init__(self, mac, scheme, logger, args): self.args = args self.n_agents = args.n_agents self.n_actions = args.n_actions self.mac = mac self.logger = logger self.last_target_update_step = 0 self.critic_training_steps = 0 self.log_stats_t = -self.args.learner_log_interval - 1 self.critic = COMACritic(scheme, args) self.target_critic = copy.deepcopy(self.critic) self.agent_params = list(mac.parameters()) self.critic_params = list(self.critic.parameters()) self.params = self.agent_params + self.critic_params self.agent_optimiser = RMSprop(params=self.agent_params, lr=args.lr, alpha=args.optim_alpha, eps=args.optim_eps) self.critic_optimiser = RMSprop(params=self.critic_params, lr=args.critic_lr, alpha=args.optim_alpha, eps=args.optim_eps) 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] critic_mask = mask.clone() mask = mask.repeat(1, 1, self.n_agents).view(-1) q_vals, critic_train_stats = self._train_critic(batch, rewards, terminated, actions, avail_actions, critic_mask, bs, max_t) actions = actions[:,:-1] mac_out = [] self.mac.init_hidden(batch.batch_size) for t in range(batch.max_seq_length - 1): agent_outs = self.mac.forward(batch, t=t) mac_out.append(agent_outs) mac_out = th.stack(mac_out, dim=1) # Concat over time # 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 # Calculated baseline q_vals = q_vals.reshape(-1, self.n_actions) pi = mac_out.view(-1, self.n_actions) baseline = (pi * q_vals).sum(-1).detach() # Calculate policy grad with mask q_taken = th.gather(q_vals, dim=1, index=actions.reshape(-1, 1)).squeeze(1) pi_taken = th.gather(pi, dim=1, index=actions.reshape(-1, 1)).squeeze(1) pi_taken[mask == 0] = 1.0 log_pi_taken = th.log(pi_taken) advantages = (q_taken - baseline).detach() coma_loss = - ((advantages * log_pi_taken) * mask).sum() / mask.sum() # Optimise agents self.agent_optimiser.zero_grad() coma_loss.backward() grad_norm = th.nn.utils.clip_grad_norm_(self.agent_params, self.args.grad_norm_clip) self.agent_optimiser.step() if (self.critic_training_steps - self.last_target_update_step) / self.args.target_update_interval >= 1.0: self._update_targets() self.last_target_update_step = self.critic_training_steps if t_env - self.log_stats_t >= self.args.learner_log_interval: ts_logged = len(critic_train_stats["critic_loss"]) for key in ["critic_loss", "critic_grad_norm", "td_error_abs", "q_taken_mean", "target_mean"]: self.logger.log_stat(key, sum(critic_train_stats[key])/ts_logged, t_env) self.logger.log_stat("advantage_mean", (advantages * mask).sum().item() / mask.sum().item(), t_env) self.logger.log_stat("coma_loss", coma_loss.item(), t_env) self.logger.log_stat("agent_grad_norm", grad_norm, t_env) self.logger.log_stat("pi_max", (pi.max(dim=1)[0] * mask).sum().item() / mask.sum().item(), t_env) self.log_stats_t = t_env def _train_critic(self, batch, rewards, terminated, actions, avail_actions, mask, bs, max_t): # Optimise critic target_q_vals = self.target_critic(batch)[:, :] targets_taken = th.gather(target_q_vals, dim=3, index=actions).squeeze(3) # Calculate td-lambda targets targets = build_td_lambda_targets(rewards, terminated, mask, targets_taken, self.n_agents, self.args.gamma, self.args.td_lambda) q_vals = th.zeros_like(target_q_vals)[:, :-1] running_log = { "critic_loss": [], "critic_grad_norm": [], "td_error_abs": [], "target_mean": [], "q_taken_mean": [], } for t in reversed(range(rewards.size(1))): mask_t = mask[:, t].expand(-1, self.n_agents) if mask_t.sum() == 0: continue q_t = self.critic(batch, t) q_vals[:, t] = q_t.view(bs, self.n_agents, self.n_actions) q_taken = th.gather(q_t, dim=3, index=actions[:, t:t+1]).squeeze(3).squeeze(1) targets_t = targets[:, t] td_error = (q_taken - targets_t.detach()) # 0-out the targets that came from padded data masked_td_error = td_error * mask_t # Normal L2 loss, take mean over actual data loss = (masked_td_error ** 2).sum() / mask_t.sum() self.critic_optimiser.zero_grad() loss.backward() grad_norm = th.nn.utils.clip_grad_norm_(self.critic_params, self.args.grad_norm_clip) self.critic_optimiser.step() self.critic_training_steps += 1 running_log["critic_loss"].append(loss.item()) running_log["critic_grad_norm"].append(grad_norm) mask_elems = mask_t.sum().item() running_log["td_error_abs"].append((masked_td_error.abs().sum().item() / mask_elems)) running_log["q_taken_mean"].append((q_taken * mask_t).sum().item() / mask_elems) running_log["target_mean"].append((targets_t * mask_t).sum().item() / mask_elems) return q_vals, running_log def _update_targets(self): self.target_critic.load_state_dict(self.critic.state_dict()) self.logger.console_logger.info("Updated target network") def cuda(self): self.mac.cuda() self.critic.cuda() self.target_critic.cuda() def save_models(self, path): self.mac.save_models(path) th.save(self.critic.state_dict(), "{}/critic.th".format(path)) th.save(self.agent_optimiser.state_dict(), "{}/agent_opt.th".format(path)) th.save(self.critic_optimiser.state_dict(), "{}/critic_opt.th".format(path)) def load_models(self, path): self.mac.load_models(path) self.critic.load_state_dict(th.load("{}/critic.th".format(path), map_location=lambda storage, loc: storage)) # Not quite right but I don't want to save target networks self.target_critic.load_state_dict(self.critic.state_dict()) self.agent_optimiser.load_state_dict(th.load("{}/agent_opt.th".format(path), map_location=lambda storage, loc: storage)) self.critic_optimiser.load_state_dict(th.load("{}/critic_opt.th".format(path), map_location=lambda storage, loc: storage))