def evaluate(net, save_domains=False, baseline=None): test_env = SubprocVecEnv([ lambda: gym.make('SysAdmin-v0', save_domain=save_domains) for i in range(config.eval_batch) ], in_series=(config.eval_batch // config.cpus), context='fork') tqdm_val = tqdm(desc='Validating', total=config.eval_problems, unit=' problems') with torch.no_grad(): net.eval() r_tot = 0. problems_finished = 0. rewards = [] steps = 0 s = test_env.reset() while problems_finished < config.eval_problems: steps += 1 if not baseline: a, v, pi, pi_full = net(s) else: a = random_action(s, baseline, config.multi) s, r, d, i = test_env.step(a) r_tot += np.sum(r) problems_finished += np.sum(d) rewards += [x['reward_total'] for x in itertools.compress(i, d)] tqdm_val.update(np.sum(d)) r_avg_ps = r_tot / (steps * config.eval_batch ) # average reward per step r_avg_pp = r_tot / problems_finished # average reward per problem net.train() if args.print_raw: rew_mean = np.mean(rewards) rew_ci95 = 1.96 * scipy.stats.sem(rewards) print(f"{rew_mean:.2f} ± {rew_ci95:.2f}") tqdm_val.close() test_env.close() eval_log = { 'reward_per_step': r_avg_ps, 'reward_per_problem': r_avg_pp, 'rewards': rewards, 'problems_finished': problems_finished, } return eval_log
def evaluate(net, split='valid', subset=None): test_env = SubprocVecEnv([lambda: gym.make('Sokograph-v0', split=split, subset=subset) for i in range(config.eval_batch)], in_series=(config.eval_batch // config.cpus), context='fork') tqdm_val = tqdm(desc='Validating', total=config.eval_problems, unit=' steps') with torch.no_grad(): net.eval() r_tot = 0. problems_solved = 0 problems_finished = 0 steps = 0 s = test_env.reset() while problems_finished < config.eval_problems: steps += 1 a, n, v, pi = net(s) actions = to_action(a, n, s, size=config.soko_size) s, r, d, i = test_env.step(actions) # print(r) r_tot += np.sum(r) problems_solved += sum('all_boxes_on_target' in x and x['all_boxes_on_target'] == True for x in i) problems_finished += np.sum(d) tqdm_val.update() r_avg = r_tot / (steps * config.eval_batch) # average reward per step problems_solved_ps = problems_solved / (steps * config.eval_batch) problems_solved_avg = problems_solved / problems_finished net.train() tqdm_val.close() test_env.close() return r_avg, problems_solved_ps, problems_solved_avg, problems_finished
def evaluate(net, planner): test_env = SubprocVecEnv([ lambda: gym.make('Boxworld-v0', plan=planner) for i in range(config.eval_batch) ], in_series=(config.eval_batch // config.cpus), context='fork') tqdm_val = tqdm(desc='Validating', total=config.eval_problems, unit=' problems') with torch.no_grad(): net.eval() r_tot = 0. problems_solved = 0. problems_finished = 0. problems_timeout = 0. steps = 0 opt_all = [] opt_solved = [] s = test_env.reset() while problems_finished < config.eval_problems: steps += 1 # for step in range(1e9): a, v, pi = net(s) s, r, d, i = test_env.step(a) # print(r) r_tot += np.sum(r) problems_solved += np.array( sum(x['d_true'] for x in i) ) # conversion to numpy for easier ZeroDivision handling (-> nan) problems_finished += np.sum(d) if planner is not None: # print([x['path_len'] / x['steps'] if x['d_true'] else 0. for x in i if x['done']]) opt_all += [ x['path_len'] / x['steps'] if x['d_true'] else 0. for x in i if x['done'] ] opt_solved += [ x['path_len'] / x['steps'] for x in i if x['d_true'] ] tqdm_val.update(np.sum(d)) problems_solved_ps = problems_solved / (steps * config.eval_batch) problems_solved_avg = problems_solved / problems_finished r_avg_ps = r_tot / (steps * config.eval_batch ) # average reward per step r_avg_pp = r_tot / problems_finished # average reward per problem opt_all_avg = np.mean(opt_all) opt_all_sem = scipy.stats.sem(opt_all) opt_solved_avg = np.mean(opt_solved) opt_solved_sem = scipy.stats.sem(opt_solved) avg_steps_to_solve = (steps * config.eval_batch) / problems_finished net.train() tqdm_val.close() test_env.close() eval_log = { 'reward_per_step': r_avg_ps, 'reward_per_problem': r_avg_pp, 'problems_solved': problems_solved_avg, 'problems_finished': problems_finished, 'solved_per_step': problems_solved_ps, 'steps_per_problem': avg_steps_to_solve, 'optimality_all': opt_all_avg, 'optimality_all_sem': opt_all_sem, 'optimality_solved': opt_solved_avg, 'optimality_solved_sem': opt_solved_sem, } return eval_log
job_name = None wandb.init(project="rrl-boxworld", name=job_name, config=config) wandb.save("*.pt") wandb.watch(net, log='all') # print(net) tot_env_steps = 0 tot_el_env_steps = 0 tqdm_main = tqdm(desc='Training', unit=' steps') s = env.reset() for step in itertools.count(start=1): a, v, pi = net(s) s, r, d, i = env.step(a) # print(r, d) # print(s) s_true = [x['s_true'] for x in i] d_true = [x['d_true'] for x in i] n_stacks = list(len(x['raw_state']) for x in i) # for the entropy regularization # update network loss, loss_pi, loss_v, loss_h, entropy, norm = net.update( r, v, pi, s_true, n_stacks, d_true, target_net) target_net.copy_weights(net, rho=config.target_rho) # save step stats
def main(): os.environ['OMP_NUM_THREADS'] = '1' envs = [ make_env(args.env_name, args.seed, i, args.log_dir) for i in range(args.num_processes) ] if args.num_processes > 1: envs = SubprocVecEnv(envs) else: envs = DummyVecEnv(envs) obs_shape = envs.observation_space.shape obs_shape = (obs_shape[0] * args.num_stack, *obs_shape[1:]) obs_numel = reduce(operator.mul, obs_shape, 1) actor_critic = Policy(obs_numel, envs.action_space) # Maxime: log some info about the model and its size modelSize = 0 for p in actor_critic.parameters(): pSize = reduce(operator.mul, p.size(), 1) modelSize += pSize print(str(actor_critic)) print('Total model size: %d' % modelSize) if envs.action_space.__class__.__name__ == "Discrete": action_shape = 1 else: action_shape = envs.action_space.shape[0] if args.cuda: actor_critic.cuda() if args.algo == 'a2c': optimizer = optim.RMSprop(actor_critic.parameters(), args.lr, eps=args.eps, alpha=args.alpha) elif args.algo == 'ppo': optimizer = optim.Adam(actor_critic.parameters(), args.lr, eps=args.eps) elif args.algo == 'acktr': optimizer = KFACOptimizer(actor_critic) rollouts = RolloutStorage(args.num_steps, args.num_processes, obs_shape, envs.action_space, actor_critic.state_size) current_obs = torch.zeros(args.num_processes, *obs_shape) def update_current_obs(obs): shape_dim0 = envs.observation_space.shape[0] obs = torch.from_numpy(obs).float() if args.num_stack > 1: current_obs[:, :-shape_dim0] = current_obs[:, shape_dim0:] current_obs[:, -shape_dim0:] = obs obs = envs.reset() update_current_obs(obs) rollouts.observations[0].copy_(current_obs) # These variables are used to compute average rewards for all processes. episode_rewards = torch.zeros([args.num_processes, 1]) final_rewards = torch.zeros([args.num_processes, 1]) if args.cuda: current_obs = current_obs.cuda() rollouts.cuda() start = time.time() for j in range(num_updates): for step in range(args.num_steps): # Sample actions value, action, action_log_prob, states = actor_critic.act( Variable(rollouts.observations[step], volatile=True), Variable(rollouts.states[step], volatile=True), Variable(rollouts.masks[step], volatile=True)) cpu_actions = action.data.squeeze(1).cpu().numpy() # Obser reward and next obs obs, reward, done, info = envs.step(cpu_actions) reward = torch.from_numpy(np.expand_dims(np.stack(reward), 1)).float() episode_rewards += reward # If done then clean the history of observations. masks = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done]) final_rewards *= masks final_rewards += (1 - masks) * episode_rewards episode_rewards *= masks if args.cuda: masks = masks.cuda() if current_obs.dim() == 4: current_obs *= masks.unsqueeze(2).unsqueeze(2) elif current_obs.dim() == 3: current_obs *= masks.unsqueeze(2) else: current_obs *= masks update_current_obs(obs) rollouts.insert(step, current_obs, states.data, action.data, action_log_prob.data, value.data, reward, masks) next_value = actor_critic( Variable(rollouts.observations[-1], volatile=True), Variable(rollouts.states[-1], volatile=True), Variable(rollouts.masks[-1], volatile=True))[0].data rollouts.compute_returns(next_value, args.use_gae, args.gamma, args.tau) if args.algo in ['a2c', 'acktr']: values, action_log_probs, dist_entropy, states = actor_critic.evaluate_actions( Variable(rollouts.observations[:-1].view(-1, *obs_shape)), Variable(rollouts.states[:-1].view(-1, actor_critic.state_size)), Variable(rollouts.masks[:-1].view(-1, 1)), Variable(rollouts.actions.view(-1, action_shape))) values = values.view(args.num_steps, args.num_processes, 1) action_log_probs = action_log_probs.view(args.num_steps, args.num_processes, 1) advantages = Variable(rollouts.returns[:-1]) - values value_loss = advantages.pow(2).mean() action_loss = -(Variable(advantages.data) * action_log_probs).mean() if args.algo == 'acktr' and optimizer.steps % optimizer.Ts == 0: # Sampled fisher, see Martens 2014 actor_critic.zero_grad() pg_fisher_loss = -action_log_probs.mean() value_noise = Variable(torch.randn(values.size())) if args.cuda: value_noise = value_noise.cuda() sample_values = values + value_noise vf_fisher_loss = -(values - Variable(sample_values.data)).pow(2).mean() fisher_loss = pg_fisher_loss + vf_fisher_loss optimizer.acc_stats = True fisher_loss.backward(retain_graph=True) optimizer.acc_stats = False optimizer.zero_grad() (value_loss * args.value_loss_coef + action_loss - dist_entropy * args.entropy_coef).backward() if args.algo == 'a2c': nn.utils.clip_grad_norm(actor_critic.parameters(), args.max_grad_norm) optimizer.step() elif args.algo == 'ppo': advantages = rollouts.returns[:-1] - rollouts.value_preds[:-1] advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-5) for e in range(args.ppo_epoch): if args.recurrent_policy: data_generator = rollouts.recurrent_generator( advantages, args.num_mini_batch) else: data_generator = rollouts.feed_forward_generator( advantages, args.num_mini_batch) for sample in data_generator: observations_batch, states_batch, actions_batch, \ return_batch, masks_batch, old_action_log_probs_batch, \ adv_targ = sample # Reshape to do in a single forward pass for all steps values, action_log_probs, dist_entropy, states = actor_critic.evaluate_actions( Variable(observations_batch), Variable(states_batch), Variable(masks_batch), Variable(actions_batch)) adv_targ = Variable(adv_targ) ratio = torch.exp(action_log_probs - Variable(old_action_log_probs_batch)) surr1 = ratio * adv_targ surr2 = torch.clamp(ratio, 1.0 - args.clip_param, 1.0 + args.clip_param) * adv_targ action_loss = -torch.min( surr1, surr2).mean() # PPO's pessimistic surrogate (L^CLIP) value_loss = (Variable(return_batch) - values).pow(2).mean() optimizer.zero_grad() (value_loss + action_loss - dist_entropy * args.entropy_coef).backward() nn.utils.clip_grad_norm(actor_critic.parameters(), args.max_grad_norm) optimizer.step() rollouts.after_update() if j % args.save_interval == 0 and args.save_dir != "": save_path = os.path.join(args.save_dir, args.algo) try: os.makedirs(save_path) except OSError: pass # A really ugly way to save a model to CPU save_model = actor_critic if args.cuda: save_model = copy.deepcopy(actor_critic).cpu() save_model = [ save_model, hasattr(envs, 'ob_rms') and envs.ob_rms or None ] torch.save(save_model, os.path.join(save_path, args.env_name + ".pt")) if j % args.log_interval == 0: end = time.time() total_num_steps = (j + 1) * args.num_processes * args.num_steps print( "Updates {}, num timesteps {}, FPS {}, mean/median reward {:.2f}/{:.2f}, min/max reward {:.2f}/{:.2f}, entropy {:.5f}, value loss {:.5f}, policy loss {:.5f}" .format(j, total_num_steps, int(total_num_steps / (end - start)), final_rewards.mean(), final_rewards.median(), final_rewards.min(), final_rewards.max(), dist_entropy.data[0], value_loss.data[0], action_loss.data[0])) if args.vis and j % args.vis_interval == 0: win = visdom_plot(total_num_steps, final_rewards.mean())
wandb.watch(net, log='all') # print(net) tot_env_steps = 0 tot_el_env_steps = 0 tqdm_main = tqdm(desc='Training', unit=' steps') s = env.reset() for step in itertools.count(start=1): a, n, v, pi = net(s) actions = to_action(a, n, s, size=config.soko_size) # print(actions) s, r, d, i = env.step(actions) s_true = [x['s_true'] for x in i] d_true = [x['d_true'] for x in i] # update network loss, loss_pi, loss_v, loss_h, entropy, norm = net.update(r, v, pi, s_true, d_true, target_net) target_net.copy_weights(net, rho=config.target_rho) # save step stats tot_env_steps += config.batch tot_el_env_steps += np.sum([x['elementary_steps'] for x in i]) tqdm_main.update() if step % config.sched_lr_rate == 0:
def main(): os.environ['OMP_NUM_THREADS'] = '1' if args.vis: from visdom import Visdom viz = Visdom() win = None envs = [ make_env(args.env_name, args.seed, i, args.log_dir, args.start_container) for i in range(args.num_processes) ] test_envs = [ make_env(args.env_name, args.seed, i, args.log_dir, args.start_container) for i in range(args.num_processes) ] if args.num_processes > 1: envs = SubprocVecEnv(envs) test_envs = SubprocVecEnv(test_envs) else: envs = DummyVecEnv(envs) test_envs = DummyVecEnv(test_envs) obs_shape = envs.observation_space.shape obs_shape = (obs_shape[0] * args.num_stack, *obs_shape[1:]) if args.saved_encoder_model: obs_shape = (args.num_stack, args.latent_space_size) obs_numel = reduce(operator.mul, obs_shape, 1) if len(obs_shape) == 3 and obs_numel > 1024: actor_critic = CNNPolicy(obs_shape[0], envs.action_space, args.recurrent_policy) else: assert not args.recurrent_policy, \ "Recurrent policy is not implemented for the MLP controller" actor_critic = MLPPolicy(obs_numel, envs.action_space) modelSize = 0 for p in actor_critic.parameters(): pSize = reduce(operator.mul, p.size(), 1) modelSize += pSize print(str(actor_critic)) print('Total model size: %d' % modelSize) if envs.action_space.__class__.__name__ == "Discrete": action_shape = 1 else: action_shape = envs.action_space.shape[0] if args.resume_experiment: print("\n############## Loading saved model ##############\n") actor_critic, ob_rms = torch.load( os.path.join(save_path, args.env_name + args.save_tag + ".pt")) tr.load(os.path.join(log_path, args.env_name + args.save_tag + ".p")) if args.cuda: actor_critic.cuda() if args.algo == 'a2c': optimizer = optim.RMSprop(actor_critic.parameters(), args.lr, eps=args.eps, alpha=args.alpha) elif args.algo == 'ppo': optimizer = optim.Adam(actor_critic.parameters(), args.lr, eps=args.eps) elif args.algo == 'acktr': optimizer = KFACOptimizer(actor_critic) print(obs_shape) rollouts = RolloutStorage(args.num_steps, args.num_processes, obs_shape, envs.action_space, actor_critic.state_size) rollouts_test = RolloutStorage(args.num_steps_test, args.num_processes, obs_shape, envs.action_space, actor_critic.state_size) current_obs = torch.zeros(args.num_processes, *obs_shape) current_obs_test = torch.zeros(args.num_processes, *obs_shape) def update_current_obs(obs, test=False): shape_dim0 = envs.observation_space.shape[0] if args.saved_encoder_model: shape_dim0 = 1 obs, _ = vae.encode(Variable(torch.cuda.FloatTensor(obs))) obs = obs.data.cpu().numpy() obs = torch.from_numpy(obs).float() if not test: if args.num_stack > 1: current_obs[:, :-shape_dim0] = current_obs[:, shape_dim0:] current_obs[:, -shape_dim0:] = obs else: if args.num_stack > 1: current_obs_test[:, : -shape_dim0] = current_obs_test[:, shape_dim0:] current_obs_test[:, -shape_dim0:] = obs obs = envs.reset() update_current_obs(obs) rollouts.observations[0].copy_(current_obs) # These variables are used to compute average rewards for all processes. episode_rewards = torch.zeros([args.num_processes, 1]) final_rewards = torch.zeros([args.num_processes, 1]) reward_avg = 0 if args.cuda: current_obs = current_obs.cuda() current_obs_test = current_obs_test.cuda() rollouts.cuda() rollouts_test.cuda() start = time.time() for j in range(num_updates): for step in range(args.num_steps): # Sample actions value, action, action_log_prob, states = actor_critic.act( Variable(rollouts.observations[step], volatile=True), Variable(rollouts.states[step], volatile=True), Variable(rollouts.masks[step], volatile=True)) cpu_actions = action.data.squeeze(1).cpu().numpy() # Observation, reward and next obs obs, reward, done, info = envs.step(cpu_actions) # Maxime: clip the reward within [0,1] for more reliable training # This code deals poorly with large reward values reward = np.clip(reward, a_min=0, a_max=None) / 400 reward = torch.from_numpy(np.expand_dims(np.stack(reward), 1)).float() episode_rewards += reward # If done then clean the history of observations. masks = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done]) final_rewards *= masks final_rewards += (1 - masks) * episode_rewards episode_rewards *= masks tr.episodes_done += args.num_processes - masks.sum() if args.cuda: masks = masks.cuda() if current_obs.dim() == 4: current_obs *= masks.unsqueeze(2).unsqueeze(2) else: current_obs *= masks update_current_obs(obs) rollouts.insert(step, current_obs, states.data, action.data, action_log_prob.data, value.data, reward, masks) next_value = actor_critic( Variable(rollouts.observations[-1], volatile=True), Variable(rollouts.states[-1], volatile=True), Variable(rollouts.masks[-1], volatile=True))[0].data rollouts.compute_returns(next_value, args.use_gae, args.gamma, args.tau) tr.iterations_done += 1 if args.algo in ['a2c', 'acktr']: values, action_log_probs, dist_entropy, states = actor_critic.evaluate_actions( Variable(rollouts.observations[:-1].view(-1, *obs_shape)), Variable(rollouts.states[0].view(-1, actor_critic.state_size)), Variable(rollouts.masks[:-1].view(-1, 1)), Variable(rollouts.actions.view(-1, action_shape))) values = values.view(args.num_steps, args.num_processes, 1) action_log_probs = action_log_probs.view(args.num_steps, args.num_processes, 1) advantages = Variable(rollouts.returns[:-1]) - values value_loss = advantages.pow(2).mean() action_loss = -(Variable(advantages.data) * action_log_probs).mean() if args.algo == 'acktr' and optimizer.steps % optimizer.Ts == 0: # Sampled fisher, see Martens 2014 actor_critic.zero_grad() pg_fisher_loss = -action_log_probs.mean() value_noise = Variable(torch.randn(values.size())) if args.cuda: value_noise = value_noise.cuda() sample_values = values + value_noise vf_fisher_loss = -(values - Variable(sample_values.data)).pow(2).mean() fisher_loss = pg_fisher_loss + vf_fisher_loss optimizer.acc_stats = True fisher_loss.backward(retain_graph=True) optimizer.acc_stats = False optimizer.zero_grad() (value_loss * args.value_loss_coef + action_loss - dist_entropy * args.entropy_coef).backward() if args.algo == 'a2c': nn.utils.clip_grad_norm(actor_critic.parameters(), args.max_grad_norm) optimizer.step() elif args.algo == 'ppo': advantages = rollouts.returns[:-1] - rollouts.value_preds[:-1] advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-5) for e in range(args.ppo_epoch): if args.recurrent_policy: data_generator = rollouts.recurrent_generator( advantages, args.num_mini_batch) else: data_generator = rollouts.feed_forward_generator( advantages, args.num_mini_batch) for sample in data_generator: observations_batch, states_batch, actions_batch, \ return_batch, masks_batch, old_action_log_probs_batch, \ adv_targ = sample # Reshape to do in a single forward pass for all steps values, action_log_probs, dist_entropy, states = actor_critic.evaluate_actions( Variable(observations_batch), Variable(states_batch), Variable(masks_batch), Variable(actions_batch)) adv_targ = Variable(adv_targ) ratio = torch.exp(action_log_probs - Variable(old_action_log_probs_batch)) surr1 = ratio * adv_targ surr2 = torch.clamp(ratio, 1.0 - args.clip_param, 1.0 + args.clip_param) * adv_targ action_loss = -torch.min( surr1, surr2).mean() # PPO's pessimistic surrogate (L^CLIP) value_loss = (Variable(return_batch) - values).pow(2).mean() optimizer.zero_grad() (value_loss + action_loss - dist_entropy * args.entropy_coef).backward() nn.utils.clip_grad_norm(actor_critic.parameters(), args.max_grad_norm) optimizer.step() rollouts.after_update() if j % args.save_interval == 0 and args.save_dir != "": # A really ugly way to save a model to CPU save_model = actor_critic if args.cuda: save_model = copy.deepcopy(actor_critic).cpu() save_model = [ save_model, hasattr(envs, 'ob_rms') and envs.ob_rms or None ] torch.save( save_model, os.path.join(save_path, args.env_name + args.save_tag + ".pt")) total_test_reward_list = [] step_test_list = [] for _ in range(args.num_tests): test_obs = test_envs.reset() update_current_obs(test_obs, test=True) rollouts_test.observations[0].copy_(current_obs_test) step_test = 0 total_test_reward = 0 while step_test < args.num_steps_test: value_test, action_test, action_log_prob_test, states_test = actor_critic.act( Variable(rollouts_test.observations[step_test], volatile=True), Variable(rollouts_test.states[step_test], volatile=True), Variable(rollouts_test.masks[step_test], volatile=True)) cpu_actions_test = action_test.data.squeeze( 1).cpu().numpy() # Observation, reward and next obs obs_test, reward_test, done_test, info_test = test_envs.step( cpu_actions_test) # masks here doesn't really matter, but still masks_test = torch.FloatTensor( [[0.0] if done_test_ else [1.0] for done_test_ in done_test]) # Maxime: clip the reward within [0,1] for more reliable training # This code deals poorly with large reward values reward_test = np.clip(reward_test, a_min=0, a_max=None) / 400 total_test_reward += reward_test[0] reward_test = torch.from_numpy( np.expand_dims(np.stack(reward_test), 1)).float() update_current_obs(obs_test) rollouts_test.insert(step_test, current_obs_test, states_test.data, action_test.data, action_log_prob_test.data,\ value_test.data, reward_test, masks_test) step_test += 1 if done_test: break #rollouts_test.reset() # Need to reinitialise with .cuda(); don't forget total_test_reward_list.append(total_test_reward) step_test_list.append(step_test) append_to(tr.test_reward, tr, sum(total_test_reward_list) / args.num_tests) append_to(tr.test_episode_len, tr, sum(step_test_list) / args.num_tests) logger.log_scalar_rl( "test_reward", tr.test_reward[0], args.sliding_wsize, [tr.episodes_done, tr.global_steps_done, tr.iterations_done]) logger.log_scalar_rl( "test_episode_len", tr.test_episode_len[0], args.sliding_wsize, [tr.episodes_done, tr.global_steps_done, tr.iterations_done]) # Saving all the MyContainer variables tr.save( os.path.join(log_path, args.env_name + args.save_tag + ".p")) if j % args.log_interval == 0: reward_avg = 0.99 * reward_avg + 0.01 * final_rewards.mean() end = time.time() tr.global_steps_done = (j + 1) * args.num_processes * args.num_steps print( "Updates {}, num timesteps {}, FPS {}, running avg reward {:.3f}, entropy {:.5f}, value loss {:.5f}, policy loss {:.5f}" .format(j, tr.global_steps_done, int(tr.global_steps_done / (end - start)), reward_avg, dist_entropy.data[0], value_loss.data[0], action_loss.data[0])) append_to(tr.pg_loss, tr, action_loss.data[0]) append_to(tr.val_loss, tr, value_loss.data[0]) append_to(tr.entropy_loss, tr, dist_entropy.data[0]) append_to(tr.train_reward_avg, tr, reward_avg) logger.log_scalar_rl( "train_pg_loss", tr.pg_loss[0], args.sliding_wsize, [tr.episodes_done, tr.global_steps_done, tr.iterations_done]) logger.log_scalar_rl( "train_val_loss", tr.val_loss[0], args.sliding_wsize, [tr.episodes_done, tr.global_steps_done, tr.iterations_done]) logger.log_scalar_rl( "train_entropy_loss", tr.entropy_loss[0], args.sliding_wsize, [tr.episodes_done, tr.global_steps_done, tr.iterations_done]) logger.log_scalar_rl( "train_reward_avg", tr.train_reward_avg[0], args.sliding_wsize, [tr.episodes_done, tr.global_steps_done, tr.iterations_done]) """ print("Updates {}, num timesteps {}, FPS {}, mean/median reward {:.1f}/{:.1f}, min/max reward {:.1f}/{:.1f}, entropy {:.5f}, value loss {:.5f}, policy loss {:.5f}". format( j, total_num_steps, int(total_num_steps / (end - start)), final_rewards.mean(), final_rewards.median(), final_rewards.min(), final_rewards.max(), dist_entropy.data[0], value_loss.data[0], action_loss.data[0]) ) """ if args.vis and j % args.vis_interval == 0: try: # Sometimes monitor doesn't properly flush the outputs win = visdom_plot(viz, win, args.log_dir, args.env_name, args.algo) except IOError: pass