def main(): # setup logger if args.resume_dir == "": date = str(datetime.datetime.now()) date = date[:date.rfind(":")].replace("-", "") \ .replace(":", "") \ .replace(" ", "_") log_dir = os.path.join(args.log_root, "log_" + date) else: log_dir = args.resume_dir hparams_file = os.path.join(log_dir, "hparams.json") checkpoints_dir = os.path.join(log_dir, "checkpoints") if not os.path.exists(log_dir): os.makedirs(log_dir) if not os.path.exists(checkpoints_dir): os.makedirs(checkpoints_dir) if args.resume_dir == "": # write hparams with open(hparams_file, "w") as f: json.dump(args.__dict__, f, indent=2) log_file = os.path.join(log_dir, "log_train.txt") logger = Logger(log_file) # logger.info(args) logger.info("The args corresponding to training process are: ") for (key, value) in vars(args).items(): logger.info("{key:20}: {value:}".format(key=key, value=value)) actor_critic = ActorCritic(args, log_dir, checkpoints_dir) actor_critic.train()
def train(rank, args, shared_model, counter, lock, optimizer=None, select_sample=True): torch.manual_seed(args.seed + rank) print("Process No : {} | Sampling : {}".format(rank, select_sample)) FloatTensor = torch.cuda.FloatTensor if args.use_cuda else torch.FloatTensor DoubleTensor = torch.cuda.DoubleTensor if args.use_cuda else torch.DoubleTensor ByteTensor = torch.cuda.ByteTensor if args.use_cuda else torch.ByteTensor env = create_mario_env(args.env_name) env.seed(args.seed + rank) model = ActorCritic(env.observation_space.shape[0], len(ACTIONS)) if args.use_cuda: model.cuda() if optimizer is None: optimizer = optim.Adam(shared_model.parameters(), lr=args.lr) model.train() state = env.reset() state = torch.from_numpy(state) done = True episode_length = 0 for num_iter in count(): if rank == 0: env.render() if num_iter % args.save_interval == 0 and num_iter > 0: print("Saving model at :" + args.save_path) torch.save(shared_model.state_dict(), args.save_path) if num_iter % ( args.save_interval * 2.5 ) == 0 and num_iter > 0 and rank == 1: # Second saver in-case first processes crashes print("Saving model for process 1 at :" + args.save_path) torch.save(shared_model.state_dict(), args.save_path) # Sync with the shared model model.load_state_dict(shared_model.state_dict()) if done: cx = Variable(torch.zeros(1, 512)).type(FloatTensor) hx = Variable(torch.zeros(1, 512)).type(FloatTensor) else: cx = Variable(cx.data).type(FloatTensor) hx = Variable(hx.data).type(FloatTensor) values = [] log_probs = [] rewards = [] entropies = [] reason = '' for step in range(args.num_steps): episode_length += 1 state_inp = Variable(state.unsqueeze(0)).type(FloatTensor) value, logit, (hx, cx) = model((state_inp, (hx, cx))) prob = F.softmax(logit, dim=-1) log_prob = F.log_softmax(logit, dim=-1) entropy = -(log_prob * prob).sum(-1, keepdim=True) entropies.append(entropy) if select_sample: action = prob.multinomial().data else: action = prob.max(-1, keepdim=True)[1].data log_prob = log_prob.gather(-1, Variable(action)) action_out = ACTIONS[action][0, 0] # print("Process: {} Action: {}".format(rank, str(action_out))) state, reward, done, _ = env.step(action_out) done = done or episode_length >= args.max_episode_length reward = max(min(reward, 50), -50) with lock: counter.value += 1 if done: episode_length = 0 env.change_level(0) state = env.reset() print("Process {} has completed.".format(rank)) env.locked_levels = [False] + [True] * 31 state = torch.from_numpy(state) values.append(value) log_probs.append(log_prob) rewards.append(reward) if done: break R = torch.zeros(1, 1) if not done: state_inp = Variable(state.unsqueeze(0)).type(FloatTensor) value, _, _ = model((state_inp, (hx, cx))) R = value.data values.append(Variable(R).type(FloatTensor)) policy_loss = 0 value_loss = 0 R = Variable(R).type(FloatTensor) gae = torch.zeros(1, 1).type(FloatTensor) for i in reversed(range(len(rewards))): R = args.gamma * R + rewards[i] advantage = R - values[i] value_loss = value_loss + 0.5 * advantage.pow(2) # Generalized Advantage Estimataion delta_t = rewards[i] + args.gamma * \ values[i + 1].data - values[i].data gae = gae * args.gamma * args.tau + delta_t policy_loss = policy_loss - \ log_probs[i] * Variable(gae).type(FloatTensor) - args.entropy_coef * entropies[i] total_loss = policy_loss + args.value_loss_coef * value_loss print("Process {} loss :".format(rank), total_loss.data) # print("Process: {} Episode: {}".format(rank, str(episode_length))) optimizer.zero_grad() (total_loss).backward() torch.nn.utils.clip_grad_norm(model.parameters(), args.max_grad_norm) ensure_shared_grads(model, shared_model) optimizer.step() print("Process {} closed.".format(rank))
def train(rank, args, shared_model, shared_scme, counter, lock, optimizer=None, select_sample=True): torch.manual_seed(args.seed + rank) print("Process No : {} | Sampling : {}".format(rank, select_sample)) FloatTensor = torch.FloatTensor# torch.cuda.FloatTensor if args.use_cuda else torch.FloatTensor DoubleTensor = torch.DoubleTensor# torch.cuda.DoubleTensor if args.use_cuda else torch.DoubleTensor ByteTensor = torch.ByteTensor# torch.cuda.ByteTensor if args.use_cuda else torch.ByteTensor savefile = os.getcwd() + '/save/scmemi_'+ args.reward_type +'/train_reward.csv' saveweights = os.getcwd() + '/save/scmemi_'+ args.reward_type +'/mario_a3c_params.pkl' env = create_mario_env(args.env_name, args.reward_type) #env.seed(args.seed + rank) model = ActorCritic(env.observation_space.shape[0], len(ACTIONS)) if optimizer is None: optimizer = optim.Adam(list(shared_model.parameters()) + list(shared_scme.parameters()), lr=args.lr) scme_model = SCME(env.observation_space.shape[0], len(ACTIONS)) model.train() scme_model.train() state = env.reset() cum_rew = 0 state = torch.from_numpy(state) done = True episode_length = 0 for num_iter in count(): #env.render() if rank == 0: if num_iter % args.save_interval == 0 and num_iter > 0: print ("Saving model at :" + saveweights) torch.save(shared_model.state_dict(), saveweights) torch.save(shared_scme.state_dict(), saveweights[:-4] + '_scme.pkl') if num_iter % (args.save_interval * 2.5) == 0 and num_iter > 0 and rank == 1: # Second saver in-case first processes crashes print ("Saving model for process 1 at :" + saveweights) torch.save(shared_model.state_dict(), saveweights) torch.save(shared_scme.state_dict(), saveweights[:-4] + '_scme.pkl') # Sync with the shared model model.load_state_dict(shared_model.state_dict()) scme_model.load_state_dict(shared_scme.state_dict()) if done: cx = Variable(torch.zeros(1, 512)).type(FloatTensor) hx = Variable(torch.zeros(1, 512)).type(FloatTensor) else: cx = Variable(cx.data).type(FloatTensor) hx = Variable(hx.data).type(FloatTensor) values = [] log_probs = [] rewards = [] entropies = [] vae_losses = [] cur_losses = [] mi_losses = [] #reason ='' for step in range(args.num_steps): episode_length += 1 state_inp = Variable(state.unsqueeze(0)).type(FloatTensor) value, logit, (hx, cx) = model((state_inp, (hx, cx))) prob = F.softmax(logit, dim=-1) log_prob = F.log_softmax(logit, dim=-1) entropy = -(log_prob * prob).sum(-1, keepdim=True) entropies.append(entropy) if select_sample: action = prob.multinomial(1).data else: action = prob.max(-1, keepdim=True)[1].data log_prob = log_prob.gather(-1, Variable(action)) action_out = int(action[0, 0].data.numpy()) state, reward, done, info = env.step(action_out) cum_rew = cum_rew + reward action_one_hot = (torch.eye(len(ACTIONS))[action_out]).view(1,-1) next_state_inp = Variable(torch.from_numpy(state).unsqueeze(0)).type(FloatTensor) pred_z, mi, mi1, actual_z, xt1_hat, xt1, xt1_mu, xt1_logvar = scme_model((state_inp, next_state_inp, action_one_hot)) vae_loss = loss_function(xt1_hat, xt1, xt1_mu, xt1_logvar) cur_loss = ((pred_z - actual_z).pow(2)).sum(-1, keepdim=True)/2/50 mi_loss = mutual(mi, mi1).sum(-1, keepdim=True)/10 done = done or episode_length >= args.max_episode_length cur_reward = (args.alpha*cur_loss).data.numpy()[0,0] mi_reward = (args.beta*mi_loss).data.numpy() reward = cur_reward + reward + mi_reward reward = max(min(reward, 50), -5) with lock: counter.value += 1 if done: episode_length = 0 # env.change_level(0) state = env.reset() with open(savefile[:-4]+'_{}.csv'.format(rank), 'a', newline='') as sfile: writer = csv.writer(sfile) writer.writerows([[cum_rew, info['x_pos']/x_norm]]) cum_rew = 0 # print ("Process {} has completed.".format(rank)) # env.locked_levels = [False] + [True] * 31 state = torch.from_numpy(state) values.append(value) log_probs.append(log_prob) rewards.append(reward) vae_losses.append(vae_loss) cur_losses.append(cur_loss) mi_losses.append(mi_loss) if done: break R = torch.zeros(1, 1) if not done: state_inp = Variable(state.unsqueeze(0)).type(FloatTensor) value, _, _ = model((state_inp, (hx, cx))) R = value.data values.append(Variable(R).type(FloatTensor)) policy_loss = 0 value_loss = 0 scme_loss = 0 R = Variable(R).type(FloatTensor) gae = torch.zeros(1, 1).type(FloatTensor) for i in reversed(range(len(rewards))): R = args.gamma * R + rewards[i] advantage = R - values[i] value_loss = value_loss + 0.5 * advantage.pow(2) # Generalized Advantage Estimataion delta_t = rewards[i] + args.gamma * values[i + 1].data - values[i].data gae = gae * args.gamma * args.tau + delta_t policy_loss = policy_loss - log_probs[i] * Variable(gae).type(FloatTensor) - args.entropy_coef * entropies[i] scme_loss = 0.01*vae_losses[i] + cur_losses[i] - mi_losses[i] total_loss = args.lambd*(policy_loss + args.value_loss_coef * value_loss) # print ("Process {} loss :".format(rank), total_loss.data) optimizer.zero_grad() # cur_optimizer.zero_grad() (total_loss + scme_loss).backward() # (curiosity_loss).backward() torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) torch.nn.utils.clip_grad_norm_(scme_model.parameters(), args.max_grad_norm) ensure_shared_grads(model, shared_model) ensure_shared_grads(scme_model, shared_scme) optimizer.step()