class QMIX: def __init__(self, args): self.n_agents = args.n_agents self.n_actions = args.n_actions self.obs_shape = args.obs_shape input_shape = self.obs_shape if args.last_action: input_shape += self.n_actions if args.reuse_networks: input_shape += self.n_agents self.eval_rnn = RNN(input_shape, args) self.target_rnn = RNN(input_shape, args) state_shape = self.obs_shape * self.n_agents self.eval_qmix_net = QMIXNet(state_shape, args) self.target_qmix_net = QMIXNet(state_shape, args) self.args = args if args.use_cuda and torch.cuda.is_available(): self.device = torch.device("cuda:0") self.eval_rnn.to(self.device) self.target_rnn.to(self.device) self.eval_qmix_net.to(self.device) self.target_qmix_net.to(self.device) else: self.device = torch.device("cpu") self.target_rnn.load_state_dict(self.eval_rnn.state_dict()) self.target_qmix_net.load_state_dict(self.eval_qmix_net.state_dict()) self.eval_parameters = list(self.eval_qmix_net.parameters()) + list( self.eval_rnn.parameters()) self.optimizer = torch.optim.Adam(self.eval_parameters, lr=args.lr) self.eval_hidden = None self.target_hidden = None print('Init algo QMIX') def learn(self, batch, max_episode_len, train_step, epsilon=None): bs = batch['o'].shape[0] self.init_hidden(bs) for key in batch.keys(): if key == 'u': batch[key] = torch.tensor(batch[key], dtype=torch.long) else: batch[key] = torch.tensor(batch[key], dtype=torch.float32) o, o_next, u, r, terminated = batch['o'], batch['o_next'], batch[ 'u'], batch['r'], batch['terminated'] if self.args.use_cuda and torch.cuda.is_available(): o = o.to(self.device) o_next = o_next.to(self.device) u = u.to(self.device) r = r.to(self.device) terminated = terminated.to(self.device) q_evals, q_targets = self._get_q_values(batch, max_episode_len) q_evals = torch.gather(q_evals, dim=3, index=u).squeeze(3) q_targets = q_targets.max(dim=3)[0] # qmix algorithm needs the total states infos to calculate the mixer network distribution # In the MPE, envs don't support the original states infos due to the Complete Observable (Not the POMDP) # So the state can be seen as the concatenation of all the agents' observations states = o.reshape((bs, self.args.episode_limit, -1)) states_next = o_next.reshape((bs, self.args.episode_limit, -1)) q_total_eval = self.eval_qmix_net(q_evals, states) q_total_target = self.target_qmix_net(q_targets, states_next) targets = r + self.args.gamma * q_total_target * (1 - terminated) td_error = targets.detach() - q_total_eval loss = td_error.pow(2).mean() self.optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_norm_(self.eval_parameters, self.args.grad_norm_clip) self.optimizer.step() if train_step > 0 and train_step % self.args.target_update_cycle == 0: self.target_rnn.load_state_dict(self.eval_rnn.state_dict()) self.target_qmix_net.load_state_dict( self.eval_qmix_net.state_dict()) def _get_q_values(self, batch, max_episode_len): bs = batch['o'].shape[0] q_evals, q_targets = [], [] for transition_idx in range(max_episode_len): inputs, inputs_next = self._get_inputs(batch, transition_idx) if self.args.use_cuda: inputs = inputs.to(self.device) inputs_next = inputs_next.to(self.device) self.eval_hidden = self.eval_hidden.to(self.device) self.target_hidden = self.target_hidden.to(self.device) q_eval, self.eval_hidden = self.eval_rnn(inputs, self.eval_hidden) q_target, self.target_hidden = self.target_rnn( inputs_next, self.target_hidden) q_eval = q_eval.view(bs, self.n_agents, -1) q_target = q_target.view(bs, self.n_agents, -1) q_evals.append(q_eval) q_targets.append(q_target) q_evals = torch.stack(q_evals, dim=1) q_targets = torch.stack(q_targets, dim=1) return q_evals, q_targets def _get_inputs(self, batch, transition_idx): obs, obs_next, u_onehot = batch['o'][:, transition_idx], \ batch['o_next'][:, transition_idx], batch['u_onehot'][:] bs = obs.shape[0] inputs, inputs_next = [], [] inputs.append(obs) inputs_next.append(obs_next) if self.args.last_action: if transition_idx == 0: inputs.append(torch.zeros_like(u_onehot[:, transition_idx])) else: inputs.append(u_onehot[:, transition_idx - 1]) inputs_next.append(u_onehot[:, transition_idx]) if self.args.reuse_networks: inputs.append( torch.eye(self.n_agents).unsqueeze(0).expand(bs, -1, -1)) inputs_next.append( torch.eye(self.n_agents).unsqueeze(0).expand(bs, -1, -1)) inputs = torch.cat([x.reshape(bs * self.n_agents, -1) for x in inputs], dim=1) inputs_next = torch.cat( [x.reshape(bs * self.n_agents, -1) for x in inputs_next], dim=1) return inputs, inputs_next def init_hidden(self, batch_size): self.eval_hidden = torch.zeros( (batch_size, self.n_agents, self.args.rnn_hidden_dim)) self.target_hidden = torch.zeros( (batch_size, self.n_agents, self.args.rnn_hidden_dim)) def get_params(self): return { 'eval_rnn': self.eval_rnn.state_dict(), 'eval_qmix_net': self.eval_qmix_net.state_dict() } def load_params(self, params_dict): # Get parameters from save_dict self.eval_rnn.load_state_dict(params_dict['eval_rnn']) self.eval_qmix_net.load_state_dict(params_dict['eval_qmix_net']) # Copy the eval networks to target networks self.target_rnn.load_state_dict(self.eval_rnn.state_dict()) self.target_qmix_net.load_state_dict(self.eval_qmix_net.state_dict())
class LIIR: def __init__(self, args): self.n_agents = args.n_agents self.n_actions = args.n_actions self.obs_shape = args.obs_shape actor_input_shape = self.obs_shape critic_input_shape = self._get_critic_input_shape() # Actor Network (RNN) if args.last_action: actor_input_shape += self.n_actions if args.reuse_networks: actor_input_shape += self.n_agents self.critic = LiirNetwork(critic_input_shape, args) self.target_critic = LiirNetwork(critic_input_shape, args) self.eval_rnn = RNN(actor_input_shape, args) self.target_rnn = RNN(actor_input_shape, args) print('Init Algo LIIR') if args.use_cuda and torch.cuda.is_available(): self.device = torch.device("cuda:0") self.eval_rnn = self.eval_rnn.to(self.device) self.target_rnn = self.target_rnn.to(self.device) self.critic = self.critic.to(self.device) self.target_critic = self.target_critic.to(self.device) else: self.device = torch.device("cpu") self.target_critic.load_state_dict(self.critic.state_dict()) self.target_rnn.load_state_dict(self.eval_rnn.state_dict()) self.agent_params = list(self.eval_rnn.parameters()) self.critic_params = list(self.critic.fc1.parameters()) + list(self.critic.fc2.parameters()) + \ list(self.critic.fc3_v_mix.parameters()) self.intrinsic_params = list(self.critic.fc3_r_in.parameters()) + list(self.critic.fc4.parameters()) self.agent_optimizer = Adam(params=self.agent_params, lr=args.actor_lr) self.critic_optimizer = Adam(params=self.critic_params, lr=args.critic_lr) self.intrinsic_optimizer = Adam(params=self.intrinsic_params, lr=args.critic_lr) # The timing of updating the target networks is controlled by the LIIR itself ('train_step' is unnecessary) self.last_target_update_step = 0 self.critic_training_steps = 0 self.args = args self.eval_hidden = None self.target_hidden = None def learn(self, batch, max_episode_len, train_step, epsilon): bs = batch['o'].shape[0] max_t = batch['o'].shape[1] for key in batch.keys(): if key == 'u': batch[key] = torch.tensor(batch[key], dtype=torch.long) else: batch[key] = torch.tensor(batch[key], dtype=torch.float32) rewards, actions, terminated = batch['r'][:, :-1], batch['u'][:, :], batch['terminated'][:, :-1] q_vals, target_mix, target_ex, v_ex, r_in = self._train_critic(batch, rewards, terminated, actions) actions = actions[:, :-1] # (bs, max_t, n_agents, shape) # ------------------ Calculate policy grad -------------------------- self.init_hidden(bs) mac_out = self._get_eval_action_prob(batch, max_t, epsilon)[:, :-1] # (bs, max_t - 1, n_agents, n_actions) mac_out = mac_out / mac_out.sum(dim=-1, keepdim=True) q_vals = q_vals.reshape(-1, 1) # (bs * max_t - 1 * n_agents, 1) pi = mac_out.view(-1, self.n_actions) pi_taken = torch.gather(pi, dim=1, index=actions.reshape(-1, 1)).squeeze(1) log_pi_taken = torch.log(pi_taken) # (bs * max_t - 1 * n_agents * 1) advantages = (target_mix.reshape(-1, 1) - q_vals).detach() advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8) agent_loss = -(advantages * log_pi_taken).mean() self.agent_optimizer.zero_grad() agent_loss.backward(retain_graph=True) self.agent_optimizer.step() # ---------------- Intrinsic loss Optimizer -------------------------- v_ex_loss = ((v_ex - target_ex.detach()) ** 2).view(-1, 1).mean() # ------- pg1 -------- self.init_hidden(bs) mac_out_old = self._get_target_action_prob(batch, max_t, epsilon)[:, :-1] mac_out_old = mac_out_old / mac_out_old.sum(dim=-1, keepdim=True) pi_old = mac_out_old.view(-1, self.n_actions) pi_old_taken = torch.gather(pi_old, dim=1, index=actions.reshape(-1, 1)).squeeze(1) log_pi_old_taken = torch.log(pi_old_taken) # (bs * max_t - 1 * n_agents * 1) log_pi_old_taken = log_pi_old_taken.reshape(-1, self.n_agents) # (bs * max_t - 1, n_agents * 1) # ------- pg2 -------- self.init_hidden(bs) mac_out_new = self._get_eval_action_prob(batch, max_t, epsilon)[:, :-1] mac_out_new = mac_out_new / mac_out_new.sum(dim=-1, keepdim=True) pi_new = mac_out_new.view(-1, self.n_actions) pi_new_taken = torch.gather(pi_new, dim=1, index=actions.reshape(-1, 1)).squeeze(1) log_pi_new_taken = torch.log(pi_new_taken) log_pi_new_taken = log_pi_new_taken.reshape(-1, self.n_agents) # (bs * max_t - 1, n_agents * 1) neglogpac_new = - log_pi_new_taken.sum(-1) # (bs * max_t - 1, 1) pi2 = log_pi_taken.reshape(-1, self.n_agents).sum(-1).clone() ratio_new = torch.exp(- pi2 - neglogpac_new) adv_ex = (target_ex - v_ex.detach()).detach() adv_ex = (adv_ex - adv_ex.mean()) / (adv_ex.std() + 1e-8) # _______ gradient for pg 1 and 2--- pg_loss1 = log_pi_old_taken.view(-1, 1).mean() pg_loss2 = (adv_ex.view(-1) * ratio_new).mean() self.target_rnn.zero_grad() pg_loss1_grad = torch.autograd.grad(pg_loss1, list(self.target_rnn.parameters())) self.eval_rnn.zero_grad() pg_loss2_grad = torch.autograd.grad(pg_loss2, list(self.eval_rnn.parameters())) total_grad = 0 for grad1, grad2 in zip(pg_loss1_grad, pg_loss2_grad): total_grad += (grad1 * grad2).sum() target_mix = target_mix.reshape(-1, max_t - 1, self.n_agents) pg_ex_loss = (total_grad.detach() * target_mix).mean() intrinsic_loss = pg_ex_loss + v_ex_loss self.intrinsic_optimizer.zero_grad() intrinsic_loss.backward() self.intrinsic_optimizer.step() self._update_policy_old() if (self.critic_training_steps - self.last_target_update_step) / self.args.target_update_cycle >= 1.0: self._update_target() self.last_target_update_step = self.critic_training_steps def _train_critic(self, batch, rewards, terminated, actions): bs = batch['o'].shape[0] max_t = batch['o'].shape[1] inputs = self._get_critic_inputs(batch) if self.args.use_cuda: inputs.to(self.device) _, target_vals, target_val_ex = self.target_critic(inputs) r_in, _, target_val_ex_opt = self.critic(inputs) r_in_taken = torch.gather(r_in, dim=3, index=actions) r_in = r_in_taken.squeeze(-1) # (bs, max_t, n_agents * 1) target_vals = target_vals.squeeze(-1) # (bs, max_t, n_agents * 1) # Here, <rewards> <terminated> --> (bs, max_t-1, 1) # And <target_vals> <target_val_ex> <r_in> --> (bs, max_t, n_agents, 1) targets_mix, targets_ex = build_td_lambda_targets(rewards, terminated, target_vals, r_in, target_val_ex, self.args.gamma, self.args.td_lambda) vals_mix = torch.zeros_like(target_vals)[:, :-1] vals_ex = target_val_ex_opt[:, :-1] for t in reversed(range(rewards.size(1))): inputs_t = self._get_critic_inputs(batch, transition_idx=t) # (bs, 1, n_agents, shape) if self.args.use_cuda: inputs_t.to(self.device) _, q_t, _ = self.critic(inputs_t) # (bs, 1, n_agents, 1) vals_mix[:, t] = q_t.view(bs, self.n_agents) # (bs, n_agents * 1) targets_t = targets_mix[:, t] td_error_loss = (q_t.view(bs, self.n_agents) - targets_t.detach()) loss = (td_error_loss ** 2).mean() self.critic_optimizer.zero_grad() loss.backward() self.critic_optimizer.step() self.critic_training_steps += 1 # Here, vals_mix, vals_ex --> (bs, max_t-1, n_agents * 1) # And, targets_mix, targets_ex --> (bs, max_t-1, n_agents * 1) return vals_mix, targets_mix, targets_ex, vals_ex, r_in def _get_critic_inputs(self, batch, transition_idx=None): """ :param transition_idx: if transition_idx is None, slice(None) makes the whole steps into the critic inputs if transition_idx is not None, ts represent the single time-step Because the LIIR Critic Network didn't choose the GRU Network, so the steps data could be used together """ bs = batch['o'].shape[0] max_t = self.args.episode_limit if transition_idx is None else 1 ts = slice(None) if transition_idx is None else slice(transition_idx, transition_idx + 1) obs, u_onehot = batch['o'][:, ts], batch['u_onehot'][:, ts] inputs = [] # States states = obs.view((bs, max_t, 1, -1)).repeat(1, 1, self.n_agents, 1) inputs.append(states) # Actions (joint actions masked out by each agent) actions = u_onehot.view((bs, max_t, 1, -1)).repeat(1, 1, self.n_agents, 1) agent_mask = (1 - torch.eye(self.n_agents)) agent_mask = agent_mask.view(-1, 1).repeat(1, self.n_actions).view(self.n_agents, -1) inputs.append(actions * agent_mask.unsqueeze(0).unsqueeze(0)) # Agent id inputs.append(torch.eye(self.n_agents).unsqueeze(0).unsqueeze(0).expand(bs, max_t, -1, -1)) inputs = torch.cat([x.reshape(bs, max_t, self.n_agents, -1) for x in inputs], dim=-1) return inputs def _get_actor_inputs(self, batch, transition_idx): # LIIR use the policy_new and policy_old to calculate the action probability respectively # So the policy doesn't need the obs_next obs, u_onehot = batch['o'][:, transition_idx], batch['u_onehot'][:] bs = obs.shape[0] # Observation inputs = [obs] if self.args.last_action: if transition_idx == 0: inputs.append(torch.zeros_like(u_onehot[:, transition_idx])) else: inputs.append(u_onehot[:, transition_idx - 1]) if self.args.reuse_networks: inputs.append(torch.eye(self.args.n_agents).unsqueeze(0).expand(bs, -1, -1)) # Since the using of GRU network, the inputs shape should be shaped as 2 dimensions inputs = torch.cat([x.reshape(bs * self.args.n_agents, -1) for x in inputs], dim=1) return inputs def _get_eval_action_prob(self, batch, max_episode_len, epsilon): bs = batch['o'].shape[0] # The available actions for each agent. In MPE, an agent could choose every action at any time-step. avail_actions = torch.ones_like(batch['u_onehot']) # (bs, episode_limit, n_agents, n_actions) --> all 1 action_prob = [] for transition_idx in range(max_episode_len): inputs = self._get_actor_inputs(batch, transition_idx) if self.args.use_cuda: inputs = inputs.to(self.device) self.eval_hidden = self.eval_hidden.to(self.device) outputs, self.eval_hidden = self.eval_rnn(inputs, self.eval_hidden) # (bs * n_agents, n_actions) outputs = outputs.view(bs, self.n_agents, -1) prob = torch.nn.functional.softmax(outputs, dim=-1) action_prob.append(prob) action_prob = torch.stack(action_prob, dim=1).cpu() # (bs, episode_limit, n_agents, n_actions) actions_num = avail_actions.sum(dim=-1, keepdim=True).float().repeat(1, 1, 1, avail_actions.shape[-1]) action_prob = (1 - epsilon) * action_prob + torch.ones_like(action_prob) * epsilon / actions_num action_prob = action_prob / action_prob.sum(dim=-1, keepdim=True) if self.args.use_cuda: action_prob = action_prob.to(self.device) return action_prob def _get_target_action_prob(self, batch, max_episode_len, epsilon): bs = batch['o'].shape[0] # The available actions for each agent. In MPE, an agent could choose every action at any time-step. avail_actions = torch.ones_like(batch['u_onehot']) # (bs, episode_limit, n_agents, n_actions) --> all 1 action_prob = [] for transition_idx in range(max_episode_len): inputs = self._get_actor_inputs(batch, transition_idx) if self.args.use_cuda: inputs = inputs.to(self.device) self.target_hidden = self.eval_hidden.to(self.device) outputs, self.target_hidden = self.target_rnn(inputs, self.eval_hidden) # (bs * n_agents, n_actions) outputs = outputs.view(bs, self.n_agents, -1) prob = torch.nn.functional.softmax(outputs, dim=-1) action_prob.append(prob) action_prob = torch.stack(action_prob, dim=1).cpu() # (bs, episode_limit, n_agents, n_actions) actions_num = avail_actions.sum(dim=-1, keepdim=True).float().repeat(1, 1, 1, avail_actions.shape[-1]) action_prob = (1 - epsilon) * action_prob + torch.ones_like(action_prob) * epsilon / actions_num action_prob = action_prob / action_prob.sum(dim=-1, keepdim=True) if self.args.use_cuda: action_prob = action_prob.to(self.device) return action_prob def _get_critic_input_shape(self): # State (concatenation of all agents' local observations) input_shape = self.obs_shape * self.n_agents # agent_id input_shape += self.n_agents # Critic Network needs current action and last action infos (default without last actions) # Joint actions (without last actions) input_shape += self.n_actions * self.n_agents return input_shape def init_hidden(self, batch_size): self.eval_hidden = torch.zeros((batch_size, self.n_agents, self.args.rnn_hidden_dim)) self.target_hidden = torch.zeros((batch_size, self.n_agents, self.args.rnn_hidden_dim)) def _update_policy_old(self): self.target_rnn.load_state_dict(self.eval_rnn.state_dict()) def _update_target(self): self.target_critic.load_state_dict(self.critic.state_dict()) def get_params(self): return {'policy_new': self.eval_rnn.state_dict(), 'critic': self.critic.state_dict()} def load_params(self, params_dict): self.eval_rnn.load_state_dict(params_dict['policy_new']) self.critic.load_state_dict(params_dict['critic']) self.target_rnn.load_state_dict(self.eval_rnn.state_dict()) self.target_critic.load_state_dict(self.critic.state_dict())
class MAAC: def __init__(self, args): self.n_agents = args.n_agents self.n_actions = args.n_actions self.obs_shape = args.obs_shape actor_input_shape = self.obs_shape # Actor Network (RNN) if args.last_action: actor_input_shape += self.n_actions if args.reuse_networks: actor_input_shape += self.n_agents self.eval_rnn = RNN(actor_input_shape, args) print('Init Algo MAAC') self.eval_critic = MaacCritic(args) self.target_critic = MaacCritic(args) if args.use_cuda and torch.cuda.is_available(): self.device = torch.device("cuda:0") self.eval_rnn.to(self.device) self.eval_critic.to(self.device) self.target_critic.to(self.device) else: self.device = torch.device("cpu") self.target_critic.load_state_dict(self.eval_critic.state_dict()) self.rnn_parameters = list(self.eval_rnn.parameters()) self.critic_parameters = list(self.eval_critic.parameters()) self.critic_optimizer = torch.optim.Adam(self.critic_parameters, lr=args.critic_lr) self.rnn_optimizer = torch.optim.Adam(self.rnn_parameters, lr=args.actor_lr) self.args = args self.loss_func = torch.nn.MSELoss() self.eval_hidden = None def learn(self, batch, max_episode_len, train_step, epsilon): bs = batch['o'].shape[0] self.init_hidden(bs) for key in batch.keys(): if key == 'u': batch[key] = torch.tensor(batch[key], dtype=torch.long) else: batch[key] = torch.tensor(batch[key], dtype=torch.float32) u = batch['u'] if self.args.use_cuda: u = u.to(self.device) critic_rets = self._train_critic(batch, train_step) q_taken, q_values = [], [] for a_i, (q_eval, q_all, regs) in zip(range(self.n_agents), critic_rets): q_taken.append(q_eval) q_values.append(q_all) q_taken = torch.stack(q_taken, dim=2).squeeze(3) q_values = torch.stack(q_values, dim=2) action_prob = self._get_action_prob(batch, max_episode_len, epsilon) pi_taken = torch.gather(action_prob, dim=3, index=u).squeeze(3) log_pi_taken = torch.log(pi_taken) # Advantage for actor(policy) optimization baseline = (q_values * action_prob).sum(dim=3, keepdim=True).squeeze(3).detach() advantage = (q_taken - baseline).detach() loss = - (advantage * log_pi_taken).mean() self.rnn_optimizer.zero_grad() disable_gradients(self.eval_critic) loss.backward() enable_gradients(self.eval_critic) torch.nn.utils.clip_grad_norm_(self.rnn_parameters, self.args.grad_norm_clip) self.rnn_optimizer.step() def _train_critic(self, batch, train_step): r, terminated = batch['r'], batch['terminated'] if self.args.use_cuda: r = r.to(self.device) terminated = terminated.to(self.device) critic_in, target_critic_in = self._get_critic_inputs(batch) q_targets = self.target_critic(target_critic_in) critic_rets = self.eval_critic(critic_in, return_all_q=True, regularize=True) q_loss = 0 for a_i, q_next, (q_eval, q_all, regs) in zip(range(self.n_agents), q_targets, critic_rets): target = r + self.args.gamma * q_next * (1 - terminated) q_loss += self.loss_func(target, q_eval) for reg in regs: q_loss += reg self.critic_optimizer.zero_grad() q_loss.backward() self.eval_critic.scale_shared_grads() torch.nn.utils.clip_grad_norm_(self.eval_critic.parameters(), self.args.grad_norm_clip * self.n_agents) self.critic_optimizer.step() if train_step > 0 and train_step % self.args.target_update_cycle == 0: self.target_critic.load_state_dict(self.eval_critic.state_dict()) return critic_rets def _get_action_prob(self, batch, max_episode_len, epsilon): bs = batch['o'].shape[0] # The available actions for each agent. In MPE, an agent could choose every action at any time-step. avail_actions = torch.ones_like(batch['u_onehot']) # (bs, episode_limit, n_agents, n_actions) --> all 1 action_prob = [] for transition_idx in range(max_episode_len): inputs = self._get_actor_inputs(batch, transition_idx) if self.args.use_cuda: inputs = inputs.to(self.device) self.eval_hidden = self.eval_hidden.to(self.device) outputs, self.eval_hidden = self.eval_rnn(inputs, self.eval_hidden) # outputs:(bs * n_agents, n_actions) outputs = outputs.view(bs, self.n_agents, -1) prob = torch.nn.functional.softmax(outputs, dim=-1) action_prob.append(prob) action_prob = torch.stack(action_prob, dim=1).cpu() # (bs, episode_limit, n_agents, n_actions) actions_num = avail_actions.sum(dim=-1, keepdim=True).float().repeat(1, 1, 1, avail_actions.shape[-1]) action_prob = (1 - epsilon) * action_prob + torch.ones_like(action_prob) * epsilon / actions_num action_prob = action_prob / action_prob.sum(dim=-1, keepdim=True) if self.args.use_cuda: action_prob = action_prob.to(self.device) return action_prob def _get_actor_inputs(self, batch, transition_idx): # Because the rnn agent actor network didn't initialize a target network, it requires none next infos obs, u_onehot = batch['o'][:, transition_idx], batch['u_onehot'][:] bs = obs.shape[0] # Observation inputs = [obs] if self.args.last_action: if transition_idx == 0: inputs.append(torch.zeros_like(u_onehot[:, transition_idx])) else: inputs.append(u_onehot[:, transition_idx - 1]) if self.args.reuse_networks: inputs.append(torch.eye(self.args.n_agents).unsqueeze(0).expand(bs, -1, -1)) # Since the using of GRU network, the inputs shape should be shaped as 2 dimensions inputs = torch.cat([x.reshape(bs * self.args.n_agents, -1) for x in inputs], dim=1) return inputs def _get_critic_inputs(self, batch): """ The MAAC algorithm handle the critic inputs with total steps (without transition_idx) """ obs, obs_next = batch['o'], batch['o_next'] # (bs, episode_limit, n_agents, obs_shape) u_onehot = batch['u_onehot'] # (bs, episode_limit, n_agents, n_actions) u_onehot_next = u_onehot[:, 1:] # (bs, episode_limit - 1, n_agents, n_actions) padded_next = torch.zeros(*u_onehot[:, -1].shape, dtype=torch.float32).unsqueeze(1) # Add a step with zeros u_onehot_next = torch.cat((u_onehot_next, padded_next), dim=1) if self.args.use_cuda: obs = obs.to(self.device) obs_next = obs_next.to(self.device) u_onehot = u_onehot.to(self.device) u_onehot_next = u_onehot_next.to(self.device) agents_obs, agents_obs_next = [], [] agents_u, agents_u_next = [], [] for a_i in range(self.n_agents): agent_obs, agent_obs_next = obs[:, :, a_i], obs_next[:, :, a_i] # (bs, episode_limit, obs_shape) agent_u, agent_u_next = u_onehot[:, :, a_i], u_onehot_next[:, :, a_i] # (bs, episode_limit, n_actions) agents_obs.append(agent_obs) agents_obs_next.append(agent_obs_next) agents_u.append(agent_u) agents_u_next.append(agent_u_next) target_critic_in = list(zip(agents_obs_next, agents_u_next)) critic_in = list(zip(agents_obs, agents_u)) return critic_in, target_critic_in def init_hidden(self, batch_size): self.eval_hidden = torch.zeros((batch_size, self.n_agents, self.args.rnn_hidden_dim)) def get_params(self): return {'eval_critic': self.eval_critic.state_dict(), 'eval_rnn': self.eval_rnn.state_dict()} def load_params(self, params_dict): # Get parameters from save_dict self.eval_rnn.load_state_dict(params_dict['eval_rnn']) self.eval_critic.load_state_dict(params_dict['eval_critic']) # Copy the eval networks to target networks self.target_critic.load_state_dict(self.target_critic.state_dict())