class Agent: def __init__(self, actor_dims, critic_dims, n_actions, n_agents, agent_idx, chkpt_dir, alpha=0.01, beta=0.01, fc1=64, fc2=64, gamma=0.95, tau=0.01): """ Args: actor_dims: critic_dims: n_actions: number of actions n_agents: agent_idx: agent index chkpt_dir: checkpoint directory alpha: learning rate beta: learning rate fc1: fc2: gamma: discount factor tau: soft update parameter """ self.gamma = gamma self.tau = tau self.n_actions = n_actions self.agent_name = 'agent_%s' % agent_idx # e.g., name = agent_1_actor self.actor = ActorNetwork(alpha, actor_dims, fc1, fc2, n_actions, chkpt_dir=chkpt_dir, name=self.agent_name + '_actor') self.critic = CriticNetwork(beta, critic_dims, fc1, fc2, n_agents, n_actions, chkpt_dir=chkpt_dir, name=self.agent_name + '_critic') self.target_actor = ActorNetwork(alpha, actor_dims, fc1, fc2, n_actions, chkpt_dir=chkpt_dir, name=self.agent_name + '_target_actor') self.target_critic = CriticNetwork(beta, critic_dims, fc1, fc2, n_agents, n_actions, chkpt_dir=chkpt_dir, name=self.agent_name + '_target_critic') # initially target networks and networks have the same parameters self.update_network_parameters(tau=1) def choose_action(self, observation): """ Args: observation: Returns: action w.r.t. the current policy and exploration """ state = T.tensor([observation], dtype=T.float).to(self.actor.device) # action of current policy actions = self.actor.forward(state) # exploration (0.1 is the parameter of the exploration) noise = 0.1 * T.rand(self.n_actions).to(self.actor.device) # print(f"action={action}, noise={noise}") action = actions + noise # action = actions return action.detach().cpu().numpy()[0] def update_network_parameters(self, tau=None): # use default tau if nothing is input if tau is None: tau = self.tau target_actor_params = self.target_actor.named_parameters() actor_params = self.actor.named_parameters() # soft update of target networks target_actor_state_dict = dict(target_actor_params) actor_state_dict = dict(actor_params) for name in actor_state_dict: actor_state_dict[name] = tau * actor_state_dict[name].clone() + \ (1 - tau) * target_actor_state_dict[ name].clone() self.target_actor.load_state_dict(actor_state_dict) target_critic_params = self.target_critic.named_parameters() critic_params = self.critic.named_parameters() target_critic_state_dict = dict(target_critic_params) critic_state_dict = dict(critic_params) for name in critic_state_dict: critic_state_dict[name] = tau * critic_state_dict[name].clone() + \ (1 - tau) * target_critic_state_dict[ name].clone() self.target_critic.load_state_dict(critic_state_dict) def save_models(self): self.actor.save_checkpoint() self.target_actor.save_checkpoint() self.critic.save_checkpoint() self.target_critic.save_checkpoint() def load_models(self): self.actor.load_checkpoint() self.target_actor.load_checkpoint() self.critic.load_checkpoint() self.target_critic.load_checkpoint()
class Agent(): def __init__(self, alpha, beta, input_dims, tau, n_actions, gamma=0.99, max_size=50000, fc1_dims=400, fc2_dims=300, batch_size=32): self.gamma = gamma self.tau = tau self.batch_size = batch_size self.alpha = alpha self.beta = beta self.memory = ReplayBuffer(max_size, input_dims, n_actions) self.noise = OUActionNoise(mu=np.zeros(n_actions)) self.actor = ActorNetwork(alpha, input_dims, fc1_dims, fc2_dims, n_actions=n_actions, name='actor') self.critic = CriticNetwork(beta, input_dims, fc1_dims, fc2_dims, n_actions=n_actions, name='critic') self.target_actor = ActorNetwork(alpha, input_dims, fc1_dims, fc2_dims, n_actions=n_actions, name='target_actor') self.target_critic = CriticNetwork(beta, input_dims, fc1_dims, fc2_dims, n_actions=n_actions, name='target_critic') self.update_network_parameters( tau=1) # for the first time target_actor and actor are same def choose_action(self, observation): self.actor.eval( ) # we are setting our actor network to eval mode because we have batch normalization layer # and we dont want to calculate statistics for that layer at this step state = T.tensor([observation], dtype=T.float).to(self.actor.device) mu = self.actor.forward(state).to(self.actor.device) mu_prime = mu + T.tensor(self.noise(), dtype=T.float).to( self.actor.device) self.actor.train() return mu_prime.cpu().detach().numpy()[0] def remember(self, state, action, reward, state_, done): self.memory.store_transition(state, action, reward, state_, done) def save_models(self): self.actor.save_checkpoint() self.target_actor.save_checkpoint() self.critic.save_checkpoint() self.target_critic.save_checkpoint() def load_models(self): self.actor.load_checkpoint() self.target_actor.load_checkpoint() self.critic.load_checkpoint() self.target_critic.load_checkpoint() def learn(self): if self.memory.mem_cntr < self.batch_size: return states, actions, rewards, states_, done = self.memory.sample_buffer( self.batch_size) states = T.tensor(states, dtype=T.float).to(self.actor.device) states_ = T.tensor(states_, dtype=T.float).to(self.actor.device) actions = T.tensor(actions, dtype=T.float).to(self.actor.device) rewards = T.tensor(rewards, dtype=T.float).to(self.actor.device) done = T.tensor(done).to(self.actor.device) target_actions = self.target_actor.forward(states_) critic_value_ = self.target_critic.forward(states_, target_actions) critic_value = self.critic.forward(states, actions) critic_value_[done] = 0.0 critic_value_ = critic_value_.view(-1) target = rewards + self.gamma * critic_value_ target = target.view(self.batch_size, 1) self.critic.optimizer.zero_grad() critic_loss = F.mse_loss(target, critic_value) critic_loss.backward() self.critic.optimizer.step() self.actor.optimizer.zero_grad() actor_loss = -self.critic.forward(states, self.actor.forward(states)) actor_loss = T.mean(actor_loss) actor_loss.backward() self.actor.optimizer.step() self.update_network_parameters( ) # sending tau None so that local tau variable there takes the value of class tau variable def update_network_parameters(self, tau=None): if tau is None: tau = self.tau actor_params = self.actor.named_parameters() critic_params = self.critic.named_parameters() target_actor_params = self.target_actor.named_parameters() target_critic_params = self.target_critic.named_parameters() critic_state_dict = dict(critic_params) actor_state_dict = dict(actor_params) target_critic_state_dict = dict(target_critic_params) target_actor_state_dict = dict(target_actor_params) for name in critic_state_dict: critic_state_dict[name] = tau * critic_state_dict[name].clone() + ( 1 - tau) * target_critic_state_dict[name].clone() for name in actor_state_dict: actor_state_dict[name] = tau * actor_state_dict[name].clone() + ( 1 - tau) * target_actor_state_dict[name].clone() self.target_critic.load_state_dict(critic_state_dict) self.target_actor.load_state_dict(actor_state_dict)
def main(): args = parse_arguments() env = gym.make(args.env_name) actor = ActorNetwork(env.observation_space.shape[0],env.action_space.n) actor.load_state_dict(torch.load(args.load_dir)) test(env,actor,args.render)
class Agent: def __init__(self, actor_dims, critic_dims, n_actions, n_agents, agent_idx, chkpt_dir, alpha=0.01, beta=0.01, fc1=64, fc2=64, gamma=0.95, tau=0.01): self.gamma = gamma self.tau = tau self.n_actions = n_actions self.agent_name = 'agent_%s' % agent_idx self.actor = ActorNetwork(alpha, actor_dims, fc1, fc2, n_actions, chkpt_dir=chkpt_dir, name=self.agent_name + '_actor') self.critic = CriticNetwork(beta, critic_dims, fc1, fc2, n_agents, n_actions, chkpt_dir=chkpt_dir, name=self.agent_name + '_critic') self.target_actor = ActorNetwork(alpha, actor_dims, fc1, fc2, n_actions, chkpt_dir=chkpt_dir, name=self.agent_name + '_target_actor') self.target_critic = CriticNetwork(beta, critic_dims, fc1, fc2, n_agents, n_actions, chkpt_dir=chkpt_dir, name=self.agent_name + '_target_critic') self.update_network_parameters(tau=1) def choose_action(self, observation): state = T.tensor([observation], dtype=T.float).to(self.actor.device) actions = self.actor.forward(state) noise = T.rand(self.n_actions).to(self.actor.device) action = actions + noise return action.detach().cpu().numpy()[0] def update_network_parameters(self, tau=None): if tau is None: tau = self.tau target_actor_params = self.target_actor.named_parameters() actor_params = self.actor.named_parameters() target_actor_state_dict = dict(target_actor_params) actor_state_dict = dict(actor_params) for name in actor_state_dict: actor_state_dict[name] = tau*actor_state_dict[name].clone() + \ (1-tau)*target_actor_state_dict[name].clone() self.target_actor.load_state_dict(actor_state_dict) target_critic_params = self.target_critic.named_parameters() critic_params = self.critic.named_parameters() target_critic_state_dict = dict(target_critic_params) critic_state_dict = dict(critic_params) for name in critic_state_dict: critic_state_dict[name] = tau*critic_state_dict[name].clone() + \ (1-tau)*target_critic_state_dict[name].clone() self.target_critic.load_state_dict(critic_state_dict) def save_models(self): self.actor.save_checkpoint() self.target_actor.save_checkpoint() self.critic.save_checkpoint() self.target_critic.save_checkpoint() def load_models(self): self.actor.load_checkpoint() self.target_actor.load_checkpoint() self.critic.load_checkpoint() self.target_critic.load_checkpoint()
class Agent(object): def __init__(self, alpha, beta, input_dims, action_bound, tau, env, gamma=0.99, n_actions=2, max_size=1000000, layer1_size=400, layer2_size=300, batch_size=64): self.gamma = gamma self.tau = tau self.memory = ReplayBuffer(max_size, input_dims, n_actions) self.batch_size = batch_size self.action_bound = action_bound self.actor = ActorNetwork(alpha, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='Actor') self.critic = CriticNetwork(beta, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='Critic') self.target_actor = ActorNetwork(alpha, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='TargetActor') self.target_critic = CriticNetwork(beta, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='TargetCritic') self.noise = OUActionNoise(mu=np.zeros(n_actions)) self.update_network_parameters(tau=1) def choose_action(self, observation): self.actor.eval() observation = T.tensor(observation, dtype=T.float).to(self.actor.device) mu = self.actor.forward(observation).to(self.actor.device) mu_prime = mu + T.tensor(self.noise(), dtype=T.float).to( self.actor.device) self.actor.train() return (mu_prime * T.tensor(self.action_bound)).cpu().detach().numpy() def remember(self, state, action, reward, new_state, done): self.memory.store_transition(state, action, reward, new_state, done) def learn(self): if self.memory.mem_cntr < self.batch_size: return state, action, reward, new_state, done = \ self.memory.sample_buffer(self.batch_size) reward = T.tensor(reward, dtype=T.float).to(self.critic.device) done = T.tensor(done).to(self.critic.device) new_state = T.tensor(new_state, dtype=T.float).to(self.critic.device) action = T.tensor(action, dtype=T.float).to(self.critic.device) state = T.tensor(state, dtype=T.float).to(self.critic.device) self.target_actor.eval() self.target_critic.eval() self.critic.eval() target_actions = self.target_actor.forward(new_state) critic_value_ = self.target_critic.forward(new_state, target_actions) critic_value = self.critic.forward(state, action) target = [] for j in range(self.batch_size): target.append(reward[j] + self.gamma * critic_value_[j] * done[j]) target = T.tensor(target).to(self.critic.device) target = target.view(self.batch_size, 1) self.critic.train() self.critic.optimizer.zero_grad() critic_loss = F.mse_loss(target, critic_value) critic_loss.backward() self.critic.optimizer.step() self.critic.eval() self.actor.optimizer.zero_grad() mu = self.actor.forward(state) self.actor.train() actor_loss = -self.critic.forward(state, mu) actor_loss = T.mean(actor_loss) actor_loss.backward() self.actor.optimizer.step() self.update_network_parameters() def update_network_parameters(self, tau=None): if tau is None: tau = self.tau actor_params = self.actor.named_parameters() critic_params = self.critic.named_parameters() target_actor_params = self.target_actor.named_parameters() target_critic_params = self.target_critic.named_parameters() critic_state_dict = dict(critic_params) actor_state_dict = dict(actor_params) target_critic_dict = dict(target_critic_params) target_actor_dict = dict(target_actor_params) for name in critic_state_dict: critic_state_dict[name] = tau*critic_state_dict[name].clone() + \ (1-tau)*target_critic_dict[name].clone() self.target_critic.load_state_dict(critic_state_dict) for name in actor_state_dict: actor_state_dict[name] = tau*actor_state_dict[name].clone() + \ (1-tau)*target_actor_dict[name].clone() self.target_actor.load_state_dict(actor_state_dict) """ #Verify that the copy assignment worked correctly target_actor_params = self.target_actor.named_parameters() target_critic_params = self.target_critic.named_parameters() critic_state_dict = dict(target_critic_params) actor_state_dict = dict(target_actor_params) print('\nActor Networks', tau) for name, param in self.actor.named_parameters(): print(name, T.equal(param, actor_state_dict[name])) print('\nCritic Networks', tau) for name, param in self.critic.named_parameters(): print(name, T.equal(param, critic_state_dict[name])) input() """ def save_models(self): self.actor.save_checkpoint() self.target_actor.save_checkpoint() self.critic.save_checkpoint() self.target_critic.save_checkpoint() def load_models(self): self.actor.load_checkpoint() self.target_actor.load_checkpoint() self.critic.load_checkpoint() self.target_critic.load_checkpoint() def check_actor_params(self): current_actor_params = self.actor.named_parameters() current_actor_dict = dict(current_actor_params) original_actor_dict = dict(self.original_actor.named_parameters()) original_critic_dict = dict(self.original_critic.named_parameters()) current_critic_params = self.critic.named_parameters() current_critic_dict = dict(current_critic_params) print('Checking Actor parameters') for param in current_actor_dict: print( param, T.equal(original_actor_dict[param], current_actor_dict[param])) print('Checking critic parameters') for param in current_critic_dict: print( param, T.equal(original_critic_dict[param], current_critic_dict[param])) input()
class Agent(): def __init__(self, alpha, beta, input_dims, tau, env, action_bound, gamma=0.99, update_actor_interval=2, warmup=1000, n_actions=2, max_size=1000000, layer1_size=400, layer2_size=300, batch_size=100, noise=0.1): self.gamma = gamma self.tau = tau self.max_action = env.action_space.high self.min_action = env.action_space.low self.memory = ReplayBuffer(max_size, input_dims, n_actions) self.batch_size = batch_size self.learn_step_cntr = 0 self.time_step = 0 self.warmup = warmup self.n_actions = n_actions self.update_actor_iter = update_actor_interval self.action_bound = action_bound self.actor = ActorNetwork(alpha, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='actor') self.critic_1 = CriticNetwork(beta, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='critic_1') self.critic_2 = CriticNetwork(beta, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='critic_2') self.target_actor = ActorNetwork(alpha, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='target_actor') self.target_critic_1 = CriticNetwork(beta, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='target_critic_1') self.target_critic_2 = CriticNetwork(beta, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='target_critic_2') self.noise = noise self.update_network_parameters(tau=1) def choose_action(self, observation): if self.time_step < self.warmup: mu = T.tensor( np.random.normal(scale=self.noise, size=(self.n_actions, ))) else: state = T.tensor(observation, dtype=T.float).to(self.actor.device) mu = self.actor.forward(state).to(self.actor.device) mu_prime = mu + T.tensor(np.random.normal(scale=self.noise), dtype=T.float).to(self.actor.device) mu_prime = T.clamp(mu_prime, self.min_action[0], self.max_action[0]) self.time_step += 1 return (mu_prime * T.tensor(self.action_bound)).cpu().detach().numpy() def remember(self, state, action, reward, new_state, done): self.memory.store_transition(state, action, reward, new_state, done) def learn(self): if self.memory.mem_cntr < self.batch_size: return state, action, reward, new_state, done = \ self.memory.sample_buffer(self.batch_size) reward = T.tensor(reward, dtype=T.float).to(self.critic_1.device) done = T.tensor(done).to(self.critic_1.device) state_ = T.tensor(new_state, dtype=T.float).to(self.critic_1.device) state = T.tensor(state, dtype=T.float).to(self.critic_1.device) action = T.tensor(action, dtype=T.float).to(self.critic_1.device) target_actions = self.target_actor.forward(state_) target_actions = target_actions + \ T.clamp(T.tensor(np.random.normal(scale=0.2)), -0.5, 0.5) target_actions = T.clamp(target_actions, self.min_action[0], self.max_action[0]) q1_ = self.target_critic_1.forward(state_, target_actions) q2_ = self.target_critic_2.forward(state_, target_actions) q1 = self.critic_1.forward(state, action) q2 = self.critic_2.forward(state, action) q1_[done] = 0.0 q2_[done] = 0.0 q1_ = q1_.view(-1) q2_ = q2_.view(-1) critic_value_ = T.min(q1_, q2_) target = reward + self.gamma * critic_value_ target = target.view(self.batch_size, 1) self.critic_1.optimizer.zero_grad() self.critic_2.optimizer.zero_grad() q1_loss = F.mse_loss(target, q1) q2_loss = F.mse_loss(target, q2) critic_loss = q1_loss + q2_loss critic_loss.backward() self.critic_1.optimizer.step() self.critic_2.optimizer.step() self.learn_step_cntr += 1 if self.learn_step_cntr % self.update_actor_iter != 0: return self.actor.optimizer.zero_grad() actor_q1_loss = self.critic_1.forward(state, self.actor.forward(state)) actor_loss = -T.mean(actor_q1_loss) actor_loss.backward() self.actor.optimizer.step() self.update_network_parameters() def update_network_parameters(self, tau=None): if tau is None: tau = self.tau actor_params = self.actor.named_parameters() critic_1_params = self.critic_1.named_parameters() critic_2_params = self.critic_2.named_parameters() target_actor_params = self.target_actor.named_parameters() target_critic_1_params = self.target_critic_1.named_parameters() target_critic_2_params = self.target_critic_2.named_parameters() critic_1 = dict(critic_1_params) critic_2 = dict(critic_2_params) actor = dict(actor_params) target_actor = dict(target_actor_params) target_critic_1 = dict(target_critic_1_params) target_critic_2 = dict(target_critic_2_params) for name in critic_1: critic_1[name] = tau*critic_1[name].clone() + \ (1-tau)*target_critic_1[name].clone() for name in critic_2: critic_2[name] = tau*critic_2[name].clone() + \ (1-tau)*target_critic_2[name].clone() for name in actor: actor[name] = tau*actor[name].clone() + \ (1-tau)*target_actor[name].clone() self.target_critic_1.load_state_dict(critic_1) self.target_critic_2.load_state_dict(critic_2) self.target_actor.load_state_dict(actor) def save_models(self): self.actor.save_checkpoint() self.target_actor.save_checkpoint() self.critic_1.save_checkpoint() self.critic_2.save_checkpoint() self.target_critic_1.save_checkpoint() self.target_critic_2.save_checkpoint() def load_models(self): self.actor.load_checkpoint() self.target_actor.load_checkpoint() self.critic_1.load_checkpoint() self.critic_2.load_checkpoint() self.target_critic_1.load_checkpoint() self.target_critic_2.load_checkpoint()
def main(args): args = parse_arguments() args.cuda = not args.no_cuda and torch.cuda.is_available() env = gym.make(args.env_name) os.environ['OMP_NUM_THREADS'] = '1' np.random.seed(args.seed) torch.manual_seed(args.seed) if args.cuda: torch.cuda.manual_seed_all(args.seed) torch.set_num_threads(1) writer = SummaryWriter(log_dir=args.save_dir) actor = ActorNetwork(env.observation_space.shape[0], env.action_space.n) critic = CriticNetwork(env.observation_space.shape[0]) if args.continue_training: try: actorState = torch.load(args.load_dir, map_location=lambda storage, loc: storage) actor.load_state_dict(actorState) except: assert False, "Unable to find a model to load" if args.cuda: actor.cuda() critic.cuda() actor_optimizer = optim.Adam(actor.parameters(), lr=args.lr) critic_optimizer = optim.Adam(critic.parameters(), lr=args.lr) N = args.nsteps eps = 1.0 obsarr = [] rewardarr = [] actionlossarr = [] actionarr = [] valuearr = [] ep_len = 0 for ep in range(args.num_episodes): done = False obs = env.reset() while not done: ep_len += 1 obs_var = Variable(torch.from_numpy(obs).float(), volatile=True) action = actor.get_action(obs_var) value = critic(obs_var) action = action.data[0] next_obs, reward, done, _ = env.step(action) if args.render: env.render() obsarr.append(obs) actionarr.append(action) rewardarr.append(reward) valuearr.append(value) obs = next_obs T = len(obsarr) G = [0] * T batch_obs = Variable(torch.from_numpy(np.stack(obsarr)).float()) batch_act = Variable(torch.from_numpy(np.array(actionarr))) logprobvar = actor.evaluate_actions(batch_obs, batch_act) valvar = critic(batch_obs) logprobvar = logprobvar.squeeze(1) valvar = valvar.squeeze(1) for t in reversed(range(T)): V = 0 if t + N < T: V = valvar[t + N].data[0] G[t] = pow(args.gamma, N) * V u = min(N, T - t) for k in range(u): G[t] += pow(args.gamma, k) * rewardarr[t + k] Gtensor = Variable(torch.FloatTensor(G)) adv = 0.01 * Gtensor - valvar.detach() action_loss = -(adv * logprobvar).mean() value_loss = (0.01 * Gtensor - valvar).pow(2).mean() actionlossarr.append(action_loss) critic_optimizer.zero_grad() value_loss.backward() torch.nn.utils.clip_grad_norm(critic.parameters(), 3) critic_optimizer.step() if ep % args.update_freq == 0: actor_optimizer.zero_grad() l = torch.cat(actionlossarr).mean() l.backward() torch.nn.utils.clip_grad_norm(actor.parameters(), 3) actor_optimizer.step() r = np.array(rewardarr).sum() / args.update_freq print("Episode: {} | Reward: {:.3f}| Length: {}".format( ep, r, ep_len / args.update_freq)) obsarr = [] rewardarr = [] actionlossarr = [] actionarr = [] ep_len = 0 if ep % 500 == 0: torch.save(actor.state_dict(), args.save_dir + '/' + args.env_name + '.pt') rm, rs, em = test(env, actor, False) writer.add_scalar('test/reward_mean', rm, ep) writer.add_scalar('test/reward_std', rs, ep) writer.add_scalar('test/ep_len_mean', em, ep) writer.export_scalars_to_json(args.save_dir + '/' + args.env_name + '_scalars.json') writer.add_scalar('train/reward', r, ep)