def __init__( self, input_size, action_size, num_env, num_step, gamma, lam=0.95, learning_rate=1e-4, ent_coef=0.01, clip_grad_norm=0.5, epoch=3, batch_size=128, ppo_eps=0.1, update_proportion=0.25, use_gae=True, use_cuda=False, use_noisy_net=False, device=None, ): self.model = CnnActorCriticNetwork(input_size, action_size, use_noisy_net) self.num_env = num_env self.action_size = action_size self.input_size = input_size self.num_step = num_step self.gamma = gamma self.lam = lam self.epoch = epoch self.batch_size = batch_size self.use_gae = use_gae self.ent_coef = ent_coef self.ppo_eps = ppo_eps self.clip_grad_norm = clip_grad_norm self.update_proportion = update_proportion self.device = device if device is not None else torch.device( 'cuda' if use_cuda else 'cpu') self.rnd = RNDModel(input_size, action_size) self.optimizer = optim.Adam(list(self.model.parameters()) + list(self.rnd.predictor.parameters()), lr=learning_rate) self.rnd = self.rnd.to(self.device) self.model = self.model.to(self.device) # varianse matrix self.action_std_eye = torch.eye(self.action_size).to(self.device) self.action_std_eye.requires_grad = False
def __init__(self, input_size, output_size, num_env, num_step, gamma, lam=0.95, learning_rate=1e-4, ent_coef=0.01, clip_grad_norm=0.5, epoch=3, batch_size=128, ppo_eps=0.1, update_proportion=0.25, use_gae=True, use_cuda=False): # Build the PPO Model self.model = PPOModel(input_size, output_size) self.num_env = num_env self.output_size = output_size self.input_size = input_size self.num_step = num_step self.gamma = gamma self.lam = lam self.epoch = epoch self.batch_size = batch_size self.use_gae = use_gae self.ent_coef = ent_coef self.ppo_eps = ppo_eps self.clip_grad_norm = clip_grad_norm self.update_proportion = update_proportion self.device = torch.device('cuda' if use_cuda else 'cpu') print("DEVICE: ", self.device) # Build the RND model self.rnd = RNDModel(input_size, output_size) # Define the optimizer (Adam) self.optimizer = optim.Adam(list(self.model.parameters()) + list(self.rnd.predictor.parameters()), lr=learning_rate) # CPU/GPU self.rnd = self.rnd.to(self.device) self.model = self.model.to(self.device)
def __init__( self, nov_rnd, input_size, output_size, use_cuda=False, rel_nov_percentile=95, freq_percentile=50, ): self.input_size = input_size self.output_size = output_size self.device = torch.device('cuda' if use_cuda else 'cpu') self.n_l = 7 self.thresh_lr = 0.1 self.rel_nov_percentile = rel_nov_percentile self.rel_nov_thresh = 10 #100 self.nov_rnd = RNDAgent(nov_rnd, use_cuda=use_cuda) self.freq_percentile = freq_percentile self.freq_thresh = 0 self.freq_rnd = RNDAgent(RNDModel(input_size, output_size), use_cuda=use_cuda) self.rel_nov_state_buf = deque(maxlen=100)
def main(): args = get_args() device = torch.device('cuda' if args.cuda else 'cpu') seed = np.random.randint(0, 100) env = ObstacleTowerEnv('../ObstacleTower/obstacletower', worker_id=seed, retro=True, config={'total-floors': 12}, greyscale=True, timeout_wait=300) env._flattener = ActionFlattener([2, 3, 2, 1]) env._action_space = env._flattener.action_space input_size = env.observation_space.shape # 4 output_size = env.action_space.n # 2 env.close() is_render = False if not os.path.exists(args.save_dir): os.makedirs(args.save_dir) model_path = os.path.join(args.save_dir, 'main.model') predictor_path = os.path.join(args.save_dir, 'main.pred') target_path = os.path.join(args.save_dir, 'main.target') writer = SummaryWriter()#log_dir=args.log_dir) discounted_reward = RewardForwardFilter(args.ext_gamma) model = CnnActorCriticNetwork(input_size, output_size, args.use_noisy_net) rnd = RNDModel(input_size, output_size) model = model.to(device) rnd = rnd.to(device) optimizer = optim.Adam(list(model.parameters()) + list(rnd.predictor.parameters()), lr=args.lr) if args.load_model: "Loading model..." if args.cuda: model.load_state_dict(torch.load(model_path)) else: model.load_state_dict(torch.load(model_path, map_location='cpu')) works = [] parent_conns = [] child_conns = [] for idx in range(args.num_worker): parent_conn, child_conn = Pipe() work = AtariEnvironment( args.env_name, is_render, idx, child_conn, sticky_action=args.sticky_action, p=args.sticky_action_prob, max_episode_steps=args.max_episode_steps) work.start() works.append(work) parent_conns.append(parent_conn) child_conns.append(child_conn) states = np.zeros([args.num_worker, 4, 84, 84]) sample_env_index = 0 # Sample Environment index to log sample_episode = 0 sample_rall = 0 sample_step = 0 sample_i_rall = 0 global_update = 0 global_step = 0 print("Load RMS =", args.load_rms) if args.load_rms: print("Loading RMS values for observation and reward normalization") with open('reward_rms.pkl', 'rb') as f: reward_rms = dill.load(f) with open('obs_rms.pkl', 'rb') as f: obs_rms = dill.load(f) else: reward_rms = RunningMeanStd() obs_rms = RunningMeanStd(shape=(1, 1, 84, 84)) # normalize observation print('Initializing observation normalization...') next_obs = [] for step in range(args.num_step * args.pre_obs_norm_steps): actions = np.random.randint(0, output_size, size=(args.num_worker,)) for parent_conn, action in zip(parent_conns, actions): parent_conn.send(action) for parent_conn in parent_conns: next_state, reward, done, realdone, log_reward = parent_conn.recv() next_obs.append(next_state[3, :, :].reshape([1, 84, 84])) if len(next_obs) % (args.num_step * args.num_worker) == 0: next_obs = np.stack(next_obs) obs_rms.update(next_obs) next_obs = [] with open('reward_rms.pkl', 'wb') as f: dill.dump(reward_rms, f) with open('obs_rms.pkl', 'wb') as f: dill.dump(obs_rms, f) print('Training...') while True: total_state, total_reward, total_done, total_next_state, total_action, total_int_reward, total_next_obs, total_ext_values, total_int_values, total_action_probs = [], [], [], [], [], [], [], [], [], [] global_step += (args.num_worker * args.num_step) global_update += 1 # Step 1. n-step rollout for _ in range(args.num_step): actions, value_ext, value_int, action_probs = get_action(model, device, np.float32(states) / 255.) for parent_conn, action in zip(parent_conns, actions): parent_conn.send(action) next_states, rewards, dones, real_dones, log_rewards, next_obs = [], [], [], [], [], [] for parent_conn in parent_conns: next_state, reward, done, real_done, log_reward = parent_conn.recv() next_states.append(next_state) rewards.append(reward) dones.append(done) real_dones.append(real_done) log_rewards.append(log_reward) next_obs.append(next_state[3, :, :].reshape([1, 84, 84])) next_states = np.stack(next_states) rewards = np.hstack(rewards) dones = np.hstack(dones) real_dones = np.hstack(real_dones) next_obs = np.stack(next_obs) # total reward = int reward + ext Reward intrinsic_reward = compute_intrinsic_reward(rnd, device, ((next_obs - obs_rms.mean) / np.sqrt(obs_rms.var)).clip(-5, 5)) intrinsic_reward = np.hstack(intrinsic_reward) sample_i_rall += intrinsic_reward[sample_env_index] total_next_obs.append(next_obs) total_int_reward.append(intrinsic_reward) total_state.append(states) total_reward.append(rewards) total_done.append(dones) total_action.append(actions) total_ext_values.append(value_ext) total_int_values.append(value_int) total_action_probs.append(action_probs) states = next_states[:, :, :, :] sample_rall += log_rewards[sample_env_index] sample_step += 1 if real_dones[sample_env_index]: sample_episode += 1 writer.add_scalar('data/reward_per_epi', sample_rall, sample_episode) writer.add_scalar('data/reward_per_rollout', sample_rall, global_update) writer.add_scalar('data/step', sample_step, sample_episode) sample_rall = 0 sample_step = 0 sample_i_rall = 0 # calculate last next value _, value_ext, value_int, _ = get_action(model, device, np.float32(states) / 255.) total_ext_values.append(value_ext) total_int_values.append(value_int) # -------------------------------------------------- total_state = np.stack(total_state).transpose([1, 0, 2, 3, 4]).reshape([-1, 4, 84, 84]) total_reward = np.stack(total_reward).transpose().clip(-1, 1) total_action = np.stack(total_action).transpose().reshape([-1]) total_done = np.stack(total_done).transpose() total_next_obs = np.stack(total_next_obs).transpose([1, 0, 2, 3, 4]).reshape([-1, 1, 84, 84]) total_ext_values = np.stack(total_ext_values).transpose() total_int_values = np.stack(total_int_values).transpose() total_logging_action_probs = np.vstack(total_action_probs) # Step 2. calculate intrinsic reward # running mean intrinsic reward total_int_reward = np.stack(total_int_reward).transpose() total_reward_per_env = np.array([discounted_reward.update(reward_per_step) for reward_per_step in total_int_reward.T]) mean, std, count = np.mean(total_reward_per_env), np.std(total_reward_per_env), len(total_reward_per_env) reward_rms.update_from_moments(mean, std ** 2, count) # normalize intrinsic reward total_int_reward /= np.sqrt(reward_rms.var) writer.add_scalar('data/int_reward_per_epi', np.sum(total_int_reward) / args.num_worker, sample_episode) writer.add_scalar('data/int_reward_per_rollout', np.sum(total_int_reward) / args.num_worker, global_update) # ------------------------------------------------------------------------------------------- # logging Max action probability writer.add_scalar('data/max_prob', total_logging_action_probs.max(1).mean(), sample_episode) # Step 3. make target and advantage # extrinsic reward calculate ext_target, ext_adv = make_train_data(total_reward, total_done, total_ext_values, args.ext_gamma, args.gae_lambda, args.num_step, args.num_worker, args.use_gae) # intrinsic reward calculate # None Episodic int_target, int_adv = make_train_data(total_int_reward, np.zeros_like(total_int_reward), total_int_values, args.int_gamma, args.gae_lambda, args.num_step, args.num_worker, args.use_gae) # add ext adv and int adv total_adv = int_adv * args.int_coef + ext_adv * args.ext_coef # ----------------------------------------------- # Step 4. update obs normalize param obs_rms.update(total_next_obs) # ----------------------------------------------- # Step 5. Training! train_model(args, device, output_size, model, rnd, optimizer, np.float32(total_state) / 255., ext_target, int_target, total_action, total_adv, ((total_next_obs - obs_rms.mean) / np.sqrt(obs_rms.var)).clip(-5, 5), total_action_probs) if global_step % (args.num_worker * args.num_step * args.save_interval) == 0: print('Now Global Step :{}'.format(global_step)) torch.save(model.state_dict(), model_path) torch.save(rnd.predictor.state_dict(), predictor_path) torch.save(rnd.target.state_dict(), target_path) """ checkpoint_list = np.array([int(re.search(r"\d+(\.\d+)?", x)[0]) for x in glob.glob(os.path.join('trained_models', args.env_name+'*.model'))]) if len(checkpoint_list) == 0: last_checkpoint = -1 else: last_checkpoint = checkpoint_list.max() next_checkpoint = last_checkpoint + 1 print("Latest Checkpoint is #{}, saving checkpoint is #{}.".format(last_checkpoint, next_checkpoint)) incre_model_path = os.path.join(args.save_dir, args.env_name + str(next_checkpoint) + '.model') incre_predictor_path = os.path.join(args.save_dir, args.env_name + str(next_checkpoint) + '.pred') incre_target_path = os.path.join(args.save_dir, args.env_name + str(next_checkpoint) + '.target') with open(incre_model_path, 'wb') as f: torch.save(model.state_dict(), f) with open(incre_predictor_path, 'wb') as f: torch.save(rnd.predictor.state_dict(), f) with open(incre_target_path, 'wb') as f: torch.save(rnd.target.state_dict(), f) """ if args.terminate and (global_step > args.terminate_steps): with open('reward_rms.pkl', 'wb') as f: dill.dump(reward_rms, f) with open('obs_rms.pkl', 'wb') as f: dill.dump(obs_rms, f) break
class RNDAgent(object): def __init__(self, input_size, output_size, num_env, num_step, gamma, lam=0.95, learning_rate=1e-4, ent_coef=0.01, clip_grad_norm=0.5, epoch=3, batch_size=128, ppo_eps=0.1, update_proportion=0.25, use_gae=True, use_cuda=False): # Build the PPO Model self.model = PPOModel(input_size, output_size) self.num_env = num_env self.output_size = output_size self.input_size = input_size self.num_step = num_step self.gamma = gamma self.lam = lam self.epoch = epoch self.batch_size = batch_size self.use_gae = use_gae self.ent_coef = ent_coef self.ppo_eps = ppo_eps self.clip_grad_norm = clip_grad_norm self.update_proportion = update_proportion self.device = torch.device('cuda' if use_cuda else 'cpu') print("DEVICE: ", self.device) # Build the RND model self.rnd = RNDModel(input_size, output_size) # Define the optimizer (Adam) self.optimizer = optim.Adam(list(self.model.parameters()) + list(self.rnd.predictor.parameters()), lr=learning_rate) # CPU/GPU self.rnd = self.rnd.to(self.device) self.model = self.model.to(self.device) def get_action(self, state): # Transform our state into a float32 tensor state = torch.Tensor(state).to(self.device) state = state.float() # Get the action dist, ext_value, int_value policy, value_ext, value_int = self.model(state) # Get action probability distribution action_prob = F.softmax(policy, dim=-1).data.cpu().numpy() # Select action action = self.random_choice_prob_index(action_prob) return action, value_ext.data.cpu().numpy().squeeze( ), value_int.data.cpu().numpy().squeeze(), policy.detach() @staticmethod def random_choice_prob_index(p, axis=1): r = np.expand_dims(np.random.rand(p.shape[1 - axis]), axis=axis) return (p.cumsum(axis=axis) > r).argmax(axis=axis) # Calculate Intrinsic reward (prediction error) def compute_intrinsic_reward(self, next_obs): next_obs = torch.FloatTensor(next_obs).to(self.device) # Get target feature target_next_feature = self.rnd.target(next_obs) # Get prediction feature predict_next_feature = self.rnd.predictor(next_obs) # Calculate intrinsic reward intrinsic_reward = (target_next_feature - predict_next_feature).pow(2).sum(1) / 2 return intrinsic_reward.data.cpu().numpy() def train_model(self, s_batch, target_ext_batch, target_int_batch, y_batch, adv_batch, next_obs_batch, old_policy): s_batch = torch.FloatTensor(s_batch).to(self.device) target_ext_batch = torch.FloatTensor(target_ext_batch).to(self.device) target_int_batch = torch.FloatTensor(target_int_batch).to(self.device) y_batch = torch.LongTensor(y_batch).to(self.device) adv_batch = torch.FloatTensor(adv_batch).to(self.device) next_obs_batch = torch.FloatTensor(next_obs_batch).to(self.device) sample_range = np.arange(len(s_batch)) forward_mse = nn.MSELoss(reduction='none') # Get old policy with torch.no_grad(): policy_old_list = torch.stack(old_policy).permute( 1, 0, 2).contiguous().view(-1, self.output_size).to(self.device) m_old = Categorical(F.softmax(policy_old_list, dim=-1)) log_prob_old = m_old.log_prob(y_batch) # ------------------------------------------------------------ for i in range(self.epoch): # Here we'll do minibatches of training np.random.shuffle(sample_range) for j in range(int(len(s_batch) / self.batch_size)): sample_idx = sample_range[self.batch_size * j:self.batch_size * (j + 1)] # -------------------------------------------------------------------------------- # for Curiosity-driven(Random Network Distillation) predict_next_state_feature, target_next_state_feature = self.rnd( next_obs_batch[sample_idx]) forward_loss = forward_mse( predict_next_state_feature, target_next_state_feature.detach()).mean(-1) # Proportion of exp used for predictor update mask = torch.rand(len(forward_loss)).to(self.device) mask = (mask < self.update_proportion).type( torch.FloatTensor).to(self.device) forward_loss = (forward_loss * mask).sum() / torch.max( mask.sum(), torch.Tensor([1]).to(self.device)) # --------------------------------------------------------------------------------- policy, value_ext, value_int = self.model(s_batch[sample_idx]) m = Categorical(F.softmax(policy, dim=-1)) log_prob = m.log_prob(y_batch[sample_idx]) ratio = torch.exp(log_prob - log_prob_old[sample_idx]) surr1 = ratio * adv_batch[sample_idx] surr2 = torch.clamp(ratio, 1.0 - self.ppo_eps, 1.0 + self.ppo_eps) * adv_batch[sample_idx] # Calculate actor loss # - J is equivalent to max J hence -torch actor_loss = -torch.min(surr1, surr2).mean() # Calculate critic loss critic_ext_loss = F.mse_loss(value_ext.sum(1), target_ext_batch[sample_idx]) critic_int_loss = F.mse_loss(value_int.sum(1), target_int_batch[sample_idx]) # Critic loss = critic E loss + critic I loss critic_loss = critic_ext_loss + critic_int_loss # Calculate the entropy # Entropy is used to improve exploration by limiting the premature convergence to suboptimal policy. entropy = m.entropy().mean() # Reset the gradients self.optimizer.zero_grad() # CALCULATE THE LOSS # Total loss = Policy gradient loss - entropy * entropy coefficient + Value coefficient * value loss + forward_loss loss = actor_loss + 0.5 * critic_loss - self.ent_coef * entropy + forward_loss # Backpropagation loss.backward() global_grad_norm_( list(self.model.parameters()) + list(self.rnd.predictor.parameters())) self.optimizer.step()
class RNDAgent(object): def __init__(self, input_size, output_size, num_env, num_step, gamma, lam=0.95, learning_rate=1e-4, ent_coef=0.01, clip_grad_norm=0.5, epoch=3, batch_size=128, ppo_eps=0.1, update_proportion=0.25, use_gae=True, use_cuda=False, use_noisy_net=False): self.model = CnnActorCriticNetwork(input_size, output_size, use_noisy_net) self.num_env = num_env self.output_size = output_size self.input_size = input_size self.num_step = num_step self.gamma = gamma self.lam = lam self.epoch = epoch self.batch_size = batch_size self.use_gae = use_gae self.ent_coef = ent_coef self.ppo_eps = ppo_eps self.clip_grad_norm = clip_grad_norm self.update_proportion = update_proportion self.device = torch.device('cuda' if use_cuda else 'cpu') self.rnd = RNDModel(input_size, output_size) self.optimizer = optim.Adam(list(self.model.parameters()) + list(self.rnd.predictor.parameters()), lr=learning_rate) self.rnd = self.rnd.to(self.device) self.model = self.model.to(self.device) def get_action(self, state): state = torch.Tensor(state).to(self.device) state = state.float() policy, value_ext, value_int = self.model(state) action_prob = F.softmax(policy, dim=-1).data.cpu().numpy() action = self.random_choice_prob_index(action_prob) return action, value_ext.data.cpu().numpy().squeeze( ), value_int.data.cpu().numpy().squeeze(), policy.detach() @staticmethod def random_choice_prob_index(p, axis=1): r = np.expand_dims(np.random.rand(p.shape[1 - axis]), axis=axis) return (p.cumsum(axis=axis) > r).argmax(axis=axis) def compute_intrinsic_reward(self, next_obs): next_obs = torch.FloatTensor(next_obs).to(self.device) target_next_feature = self.rnd.target(next_obs) predict_next_feature = self.rnd.predictor(next_obs) intrinsic_reward = (target_next_feature - predict_next_feature).pow(2).sum(1) / 2 return intrinsic_reward.data.cpu().numpy() def train_model(self, s_batch, target_ext_batch, target_int_batch, y_batch, adv_batch, next_obs_batch, old_policy): s_batch = torch.FloatTensor(s_batch).to(self.device) target_ext_batch = torch.FloatTensor(target_ext_batch).to(self.device) target_int_batch = torch.FloatTensor(target_int_batch).to(self.device) y_batch = torch.LongTensor(y_batch).to(self.device) adv_batch = torch.FloatTensor(adv_batch).to(self.device) next_obs_batch = torch.FloatTensor(next_obs_batch).to(self.device) sample_range = np.arange(len(s_batch)) forward_mse = nn.MSELoss(reduction='none') with torch.no_grad(): policy_old_list = torch.stack(old_policy).permute( 1, 0, 2).contiguous().view(-1, self.output_size).to(self.device) m_old = Categorical(F.softmax(policy_old_list, dim=-1)) log_prob_old = m_old.log_prob(y_batch) # ------------------------------------------------------------ for i in range(self.epoch): np.random.shuffle(sample_range) for j in range(int(len(s_batch) / self.batch_size)): sample_idx = sample_range[self.batch_size * j:self.batch_size * (j + 1)] # -------------------------------------------------------------------------------- # for Curiosity-driven(Random Network Distillation) predict_next_state_feature, target_next_state_feature = self.rnd( next_obs_batch[sample_idx]) forward_loss = forward_mse( predict_next_state_feature, target_next_state_feature.detach()).mean(-1) # Proportion of exp used for predictor update mask = torch.rand(len(forward_loss)).to(self.device) mask = (mask < self.update_proportion).type( torch.FloatTensor).to(self.device) forward_loss = (forward_loss * mask).sum() / torch.max( mask.sum(), torch.Tensor([1]).to(self.device)) # --------------------------------------------------------------------------------- policy, value_ext, value_int = self.model(s_batch[sample_idx]) m = Categorical(F.softmax(policy, dim=-1)) log_prob = m.log_prob(y_batch[sample_idx]) ratio = torch.exp(log_prob - log_prob_old[sample_idx]) surr1 = ratio * adv_batch[sample_idx] surr2 = torch.clamp(ratio, 1.0 - self.ppo_eps, 1.0 + self.ppo_eps) * adv_batch[sample_idx] actor_loss = -torch.min(surr1, surr2).mean() critic_ext_loss = F.mse_loss(value_ext.sum(1), target_ext_batch[sample_idx]) critic_int_loss = F.mse_loss(value_int.sum(1), target_int_batch[sample_idx]) critic_loss = critic_ext_loss + critic_int_loss entropy = m.entropy().mean() self.optimizer.zero_grad() loss = actor_loss + 0.5 * critic_loss - self.ent_coef * entropy + forward_loss loss.backward() global_grad_norm_( list(self.model.parameters()) + list(self.rnd.predictor.parameters())) self.optimizer.step()
def main(): args = get_args() device = torch.device('cuda' if args.cuda else 'cpu') env = gym.make(args.env_name) input_size = env.observation_space.shape # 4 output_size = env.action_space.n # 2 if 'Breakout' in args.env_name: output_size -= 1 env.close() is_render = False if not os.path.exists(args.save_dir): os.makedirs(args.save_dir) model_path = os.path.join(args.save_dir, args.env_name + '.model') predictor_path = os.path.join(args.save_dir, args.env_name + '.pred') target_path = os.path.join(args.save_dir, args.env_name + '.target') writer = SummaryWriter(log_dir=args.log_dir) reward_rms = RunningMeanStd() obs_rms = RunningMeanStd(shape=(1, 1, 84, 84)) discounted_reward = RewardForwardFilter(args.ext_gamma) model = CnnActorCriticNetwork(input_size, output_size, args.use_noisy_net) rnd = RNDModel(input_size, output_size) model = model.to(device) rnd = rnd.to(device) optimizer = optim.Adam(list(model.parameters()) + list(rnd.predictor.parameters()), lr=args.lr) if args.load_model: if args.cuda: model.load_state_dict(torch.load(model_path)) else: model.load_state_dict(torch.load(model_path, map_location='cpu')) works = [] parent_conns = [] child_conns = [] for idx in range(args.num_worker): parent_conn, child_conn = Pipe() work = AtariEnvironment(args.env_name, is_render, idx, child_conn, sticky_action=args.sticky_action, p=args.sticky_action_prob, max_episode_steps=args.max_episode_steps) work.start() works.append(work) parent_conns.append(parent_conn) child_conns.append(child_conn) states = np.zeros([args.num_worker, 4, 84, 84]) sample_env_index = 0 # Sample Environment index to log sample_episode = 0 sample_rall = 0 sample_step = 0 sample_i_rall = 0 global_update = 0 global_step = 0 # normalize observation print('Initializes observation normalization...') next_obs = [] for step in range(args.num_step * args.pre_obs_norm_steps): actions = np.random.randint(0, output_size, size=(args.num_worker, )) for parent_conn, action in zip(parent_conns, actions): parent_conn.send(action) for parent_conn in parent_conns: next_state, reward, done, realdone, log_reward = parent_conn.recv() next_obs.append(next_state[3, :, :].reshape([1, 84, 84])) if len(next_obs) % (args.num_step * args.num_worker) == 0: next_obs = np.stack(next_obs) obs_rms.update(next_obs) next_obs = [] print('Training...') while True: total_state, total_reward, total_done, total_next_state, total_action, total_int_reward, total_next_obs, total_ext_values, total_int_values, total_action_probs = [], [], [], [], [], [], [], [], [], [] global_step += (args.num_worker * args.num_step) global_update += 1 # Step 1. n-step rollout for _ in range(args.num_step): actions, value_ext, value_int, action_probs = get_action( model, device, np.float32(states) / 255.) for parent_conn, action in zip(parent_conns, actions): parent_conn.send(action) next_states, rewards, dones, real_dones, log_rewards, next_obs = [], [], [], [], [], [] for parent_conn in parent_conns: next_state, reward, done, real_done, log_reward = parent_conn.recv( ) next_states.append(next_state) rewards.append(reward) dones.append(done) real_dones.append(real_done) log_rewards.append(log_reward) next_obs.append(next_state[3, :, :].reshape([1, 84, 84])) next_states = np.stack(next_states) rewards = np.hstack(rewards) dones = np.hstack(dones) real_dones = np.hstack(real_dones) next_obs = np.stack(next_obs) # total reward = int reward + ext Reward intrinsic_reward = compute_intrinsic_reward( rnd, device, ((next_obs - obs_rms.mean) / np.sqrt(obs_rms.var)).clip(-5, 5)) intrinsic_reward = np.hstack(intrinsic_reward) sample_i_rall += intrinsic_reward[sample_env_index] total_next_obs.append(next_obs) total_int_reward.append(intrinsic_reward) total_state.append(states) total_reward.append(rewards) total_done.append(dones) total_action.append(actions) total_ext_values.append(value_ext) total_int_values.append(value_int) total_action_probs.append(action_probs) states = next_states[:, :, :, :] sample_rall += log_rewards[sample_env_index] sample_step += 1 if real_dones[sample_env_index]: sample_episode += 1 writer.add_scalar('data/reward_per_epi', sample_rall, sample_episode) writer.add_scalar('data/reward_per_rollout', sample_rall, global_update) writer.add_scalar('data/step', sample_step, sample_episode) sample_rall = 0 sample_step = 0 sample_i_rall = 0 # calculate last next value _, value_ext, value_int, _ = get_action(model, device, np.float32(states) / 255.) total_ext_values.append(value_ext) total_int_values.append(value_int) # -------------------------------------------------- total_state = np.stack(total_state).transpose([1, 0, 2, 3, 4]).reshape( [-1, 4, 84, 84]) total_reward = np.stack(total_reward).transpose().clip(-1, 1) total_action = np.stack(total_action).transpose().reshape([-1]) total_done = np.stack(total_done).transpose() total_next_obs = np.stack(total_next_obs).transpose( [1, 0, 2, 3, 4]).reshape([-1, 1, 84, 84]) total_ext_values = np.stack(total_ext_values).transpose() total_int_values = np.stack(total_int_values).transpose() total_logging_action_probs = np.vstack(total_action_probs) # Step 2. calculate intrinsic reward # running mean intrinsic reward total_int_reward = np.stack(total_int_reward).transpose() total_reward_per_env = np.array([ discounted_reward.update(reward_per_step) for reward_per_step in total_int_reward.T ]) mean, std, count = np.mean(total_reward_per_env), np.std( total_reward_per_env), len(total_reward_per_env) reward_rms.update_from_moments(mean, std**2, count) # normalize intrinsic reward total_int_reward /= np.sqrt(reward_rms.var) writer.add_scalar('data/int_reward_per_epi', np.sum(total_int_reward) / args.num_worker, sample_episode) writer.add_scalar('data/int_reward_per_rollout', np.sum(total_int_reward) / args.num_worker, global_update) # ------------------------------------------------------------------------------------------- # logging Max action probability writer.add_scalar('data/max_prob', total_logging_action_probs.max(1).mean(), sample_episode) # Step 3. make target and advantage # extrinsic reward calculate ext_target, ext_adv = make_train_data(total_reward, total_done, total_ext_values, args.ext_gamma, args.gae_lambda, args.num_step, args.num_worker, args.use_gae) # intrinsic reward calculate # None Episodic int_target, int_adv = make_train_data(total_int_reward, np.zeros_like(total_int_reward), total_int_values, args.int_gamma, args.gae_lambda, args.num_step, args.num_worker, args.use_gae) # add ext adv and int adv total_adv = int_adv * args.int_coef + ext_adv * args.ext_coef # ----------------------------------------------- # Step 4. update obs normalize param obs_rms.update(total_next_obs) # ----------------------------------------------- # Step 5. Training! train_model(args, device, output_size, model, rnd, optimizer, np.float32(total_state) / 255., ext_target, int_target, total_action, total_adv, ((total_next_obs - obs_rms.mean) / np.sqrt(obs_rms.var)).clip(-5, 5), total_action_probs) if global_step % (args.num_worker * args.num_step * args.save_interval) == 0: print('Now Global Step :{}'.format(global_step)) torch.save(model.state_dict(), model_path) torch.save(rnd.predictor.state_dict(), predictor_path) torch.save(rnd.target.state_dict(), target_path)
def __init__(self, input_size, output_size, seed, num_env, pre_obs_norm_step, num_step, gamma=0.99, gamma_int=0.99, lam=0.95, int_coef=1., ext_coef=2., ent_coef=0.001, cliprange=0.1, max_grad_norm=0.0, lr=1e-4, nepochs=4, batch_size=128, update_proportion=0.25, use_gae=True): self.num_env = num_env self.output_size = output_size self.input_size = input_size self.seed = np.random.seed(seed) self.pre_obs_norm_step = pre_obs_norm_step self.num_step = num_step self.gamma = gamma self.gamma_int = gamma_int self.lam = lam self.nepochs = nepochs self.batch_size = batch_size self.use_gae = use_gae self.int_coef = int_coef self.ext_coef = ext_coef self.ent_coef = ent_coef self.cliprange = cliprange self.max_grad_norm = max_grad_norm self.update_proportion = update_proportion self.device = torch.device( "cuda" if torch.cuda.is_available() else "cpu") self.model = CnnActorCritic(input_size, output_size, seed).to(self.device) self.rnd = RNDModel(input_size, output_size, seed).to(self.device) self.optimizer = optim.Adam(list(self.model.parameters()) + list(self.rnd.predictor.parameters()), lr=lr) self.rff_int = RewardForwardFilter(gamma) #self.rff_rms_int = RunningMeanStd() #self.obs_rms = RunningMeanStd(shape=(1,84,84)) self.rff_rms_int = RunningMeanStd_openAI() self.obs_rms = RunningMeanStd_openAI(shape=(1, 84, 84)) self.rooms = None self.n_rooms = [] self.best_nrooms = -np.inf self.scores = [] self.scores_window = deque(maxlen=100) self.stats = defaultdict(float) # Count episodes and timesteps self.stats['epcount'] = 0 self.stats['tcount'] = 0
class RNDagent(object): def __init__(self, input_size, output_size, seed, num_env, pre_obs_norm_step, num_step, gamma=0.99, gamma_int=0.99, lam=0.95, int_coef=1., ext_coef=2., ent_coef=0.001, cliprange=0.1, max_grad_norm=0.0, lr=1e-4, nepochs=4, batch_size=128, update_proportion=0.25, use_gae=True): self.num_env = num_env self.output_size = output_size self.input_size = input_size self.seed = np.random.seed(seed) self.pre_obs_norm_step = pre_obs_norm_step self.num_step = num_step self.gamma = gamma self.gamma_int = gamma_int self.lam = lam self.nepochs = nepochs self.batch_size = batch_size self.use_gae = use_gae self.int_coef = int_coef self.ext_coef = ext_coef self.ent_coef = ent_coef self.cliprange = cliprange self.max_grad_norm = max_grad_norm self.update_proportion = update_proportion self.device = torch.device( "cuda" if torch.cuda.is_available() else "cpu") self.model = CnnActorCritic(input_size, output_size, seed).to(self.device) self.rnd = RNDModel(input_size, output_size, seed).to(self.device) self.optimizer = optim.Adam(list(self.model.parameters()) + list(self.rnd.predictor.parameters()), lr=lr) self.rff_int = RewardForwardFilter(gamma) #self.rff_rms_int = RunningMeanStd() #self.obs_rms = RunningMeanStd(shape=(1,84,84)) self.rff_rms_int = RunningMeanStd_openAI() self.obs_rms = RunningMeanStd_openAI(shape=(1, 84, 84)) self.rooms = None self.n_rooms = [] self.best_nrooms = -np.inf self.scores = [] self.scores_window = deque(maxlen=100) self.stats = defaultdict(float) # Count episodes and timesteps self.stats['epcount'] = 0 self.stats['tcount'] = 0 def collect_random_statistics(self, envs): """Initializes observation normalization with data from random agent.""" all_ob = [] all_ob.append(envs.reset()) for _ in range(self.pre_obs_norm_step): actions = np.random.randint(0, self.output_size, size=(self.num_env, )) ob, _, _, _ = envs.step(actions) all_ob.append(ob) if len(all_ob) % (128 * self.num_env) == 0: ob_ = np.asarray(all_ob).astype(np.float32).reshape( (-1, *envs.observation_space.shape)) self.obs_rms.update(ob_[:, -1:, :, :]) all_ob.clear() def act(self, state, action=None, calc_ent=False): """Returns dict of trajectory info. Shape ====== state (uint8) : (batch_size, framestack=4, 84, 84) Returns example {'a': tensor([10, 5, 1]), 'ent': None, 'log_pi_a': tensor([-2.8904, -2.8904, -2.8904], grad_fn=<SqueezeBackward1>), 'v_ext': tensor([0.0012, 0.0012, 0.0012], grad_fn=<SqueezeBackward0>), 'v_int': tensor([-0.0013, -0.0013, -0.0013], grad_fn=<SqueezeBackward0>)} """ #state = torch.FloatTensor(state / 255).to(self.device) assert state.dtype == 'uint8' state = torch.tensor(state / 255., dtype=torch.float, device=self.device) #state = torch.from_numpy(state /255).float().to(self.device) action_probs, value_ext, value_int = self.model(state) dist = Categorical(action_probs) if action is None: action = dist.sample() log_prob = dist.log_prob(action) entropy = dist.entropy() if calc_ent else None return { 'a': action, 'log_pi_a': log_prob, 'ent': entropy, 'v_ext': value_ext.squeeze(), 'v_int': value_int.squeeze() } def compute_intrinsic_reward(self, next_obs): """next_obs is the latest frame and must be normalized by RunningMeanStd(shape=(1, 84, 84)) Shape ====== next_obs : (batch_size, 1, 84, 84) """ next_obs = torch.tensor(next_obs, dtype=torch.float, device=self.device) #next_obs = torch.FloatTensor(next_obs).to(self.device) target_next_feature = self.rnd.target(next_obs) predict_next_feature = self.rnd.predictor(next_obs) intrinsic_reward = (target_next_feature - predict_next_feature).pow(2).mean( 1) ### MSE --- Issues #intrinsic_reward = (target_next_feature - predict_next_feature).pow(2).sum(1) / 2 return intrinsic_reward.data.cpu().numpy() def step(self, envs): """ """ # Step 1. n-step rollout next_obs_batch, int_reward_batch, state_batch, reward_batch, done_batch, action_batch, values_ext_batch, values_int_batch, log_prob_old_batch = [],[],[],[],[],[],[],[],[] epinfos = [] states = envs.reset() for _ in range(self.num_step): traj_info = self.act(states) log_prob_old = traj_info['log_pi_a'].detach().cpu().numpy() actions = traj_info['a'].cpu().numpy() value_ext = traj_info['v_ext'].detach().cpu().numpy() value_int = traj_info['v_int'].detach().cpu().numpy() next_states, rewards, dones, infos = envs.step(actions) next_obs = next_states[:, -1:, :, :] intrinsic_reward = self.compute_intrinsic_reward( ((next_obs - self.obs_rms.mean) / (np.sqrt(self.obs_rms.var))).clip(-5, 5)) #+1e-10 next_obs_batch.append(next_obs) int_reward_batch.append(intrinsic_reward) state_batch.append(states) reward_batch.append(rewards) done_batch.append(dones) action_batch.append(actions) values_ext_batch.append(value_ext) values_int_batch.append(value_int) log_prob_old_batch.append(log_prob_old) for info in infos: if 'episode' in info: epinfos.append(info['episode']) states = next_states # calculate last next value last_traj_info = self.act(states) values_ext_batch.append(last_traj_info['v_ext'].detach().cpu().numpy()) values_int_batch.append(last_traj_info['v_int'].detach().cpu().numpy()) # convert to numpy array and transpose (num_env, num_step) from (num_step, num_env) for the later calculation # For self.update() state_batch = np.stack(state_batch).transpose(1, 0, 2, 3, 4).reshape( -1, 4, 84, 84) next_obs_batch = np.stack(next_obs_batch).transpose(1, 0, 2, 3, 4).reshape( -1, 1, 84, 84) action_batch = np.stack(action_batch).transpose().reshape(-1, ) log_prob_old_batch = np.stack(log_prob_old_batch).transpose().reshape( -1, ) # For get_advantage_and_value_target_from() reward_batch = np.stack(reward_batch).transpose() done_batch = np.stack(done_batch).transpose() values_ext_batch = np.stack(values_ext_batch).transpose() values_int_batch = np.stack(values_int_batch).transpose() # -------------------------------------------------- # Step 2. calculate intrinsic reward # running estimate of the intrinsic returns int_reward_batch = np.stack(int_reward_batch).transpose() discounted_reward_per_env = np.array([ self.rff_int.update(reward_per_step) for reward_per_step in int_reward_batch.T[::-1] ]) mean, std, count = np.mean(discounted_reward_per_env), np.std( discounted_reward_per_env), len(discounted_reward_per_env) self.rff_rms_int.update_from_moments(mean, std**2, count) ### THINK ddof ! # normalize intrinsic reward int_reward_batch /= np.sqrt(self.rff_rms_int.var) # ------------------------------------------------------------------------------------------- # Step 3. make target and advantage # extrinsic reward calculate ext_target, ext_adv = get_advantage_and_value_target_from( reward_batch, done_batch, values_ext_batch, self.gamma, self.lam, self.num_step, self.num_env, self.use_gae) # intrinsic reward calculate # None Episodic int_target, int_adv = get_advantage_and_value_target_from( int_reward_batch, np.zeros_like(int_reward_batch), values_int_batch, self.gamma_int, self.lam, self.num_step, self.num_env, self.use_gae) # add ext adv and int adv total_advs = self.int_coef * int_adv + self.ext_coef * ext_adv # ----------------------------------------------- # Step 4. update obs normalize param self.obs_rms.update(next_obs_batch) # ----------------------------------------------- # Step 5. Train loss_infos = self.update( state_batch, ext_target, int_target, action_batch, total_advs, ((next_obs_batch - self.obs_rms.mean) / (np.sqrt(self.obs_rms.var))).clip(-5, 5), #+1e-10 log_prob_old_batch) # ----------------------------------------------- # Collects info for reporting. vals_info = dict( advextmean=ext_adv.mean(), retextmean=ext_target.mean(), advintmean=int_adv.mean(), retintmean=int_target.mean(), rewintsample=int_reward_batch[1] # env_number = 1 ) # Some reporting logic for epinfo in epinfos: #if self.testing: # self.I.statlists['eprew_test'].append(epinfo['r']) # self.I.statlists['eplen_test'].append(epinfo['l']) #else: if "visited_rooms" in epinfo: self.n_rooms.append(len(epinfo["visited_rooms"])) if self.best_nrooms is None: self.best_nrooms = len(epinfo["visited_rooms"]) elif len(epinfo["visited_rooms"]) > self.best_nrooms: self.best_nrooms = len(epinfo["visited_rooms"]) self.rooms = sorted(list(epinfo["visited_rooms"])) #self.rooms += list(epinfo["visited_rooms"]) #self.rooms = sorted(list(set(self.rooms))) #self.I.statlists['eprooms'].append(len(epinfo["visited_rooms"])) self.scores.append(epinfo['r']) self.scores_window.append(epinfo['r']) self.stats['epcount'] += 1 self.stats['tcount'] += epinfo['l'] #self.I.statlists['eprew'].append(epinfo['r']) #self.I.statlists['eplen'].append(epinfo['l']) #self.stats['rewtotal'] += epinfo['r'] return {'loss': loss_infos, 'vals': vals_info} def update(self, s_batch, target_ext_batch, target_int_batch, action_batch, adv_batch, next_obs_batch, log_prob_old_batch): #s_batch = torch.FloatTensor(s_batch).to(self.device) target_ext_batch = torch.FloatTensor(target_ext_batch).to(self.device) target_int_batch = torch.FloatTensor(target_int_batch).to(self.device) action_batch = torch.LongTensor(action_batch).to(self.device) adv_batch = torch.FloatTensor(adv_batch).to(self.device) next_obs_batch = torch.FloatTensor(next_obs_batch).to(self.device) log_prob_old_batch = torch.FloatTensor(log_prob_old_batch).to( self.device) sample_range = np.arange(len(s_batch)) forward_mse = nn.MSELoss(reduction='none') loss_infos = defaultdict(list) for _ in range(self.nepochs): np.random.shuffle(sample_range) for j in range(int(len(s_batch) / self.batch_size)): sample_idx = sample_range[self.batch_size * j:self.batch_size * (j + 1)] # -------------------------------------------------------------------------------- # for Curiosity-driven(Random Network Distillation) predict_next_state_feature, target_next_state_feature = self.rnd( next_obs_batch[sample_idx]) forward_loss = forward_mse( predict_next_state_feature, target_next_state_feature.detach()).mean(-1) # Proportion of exp used for predictor update --- cf. cnn_policy_param_matched.py mask = torch.rand(len(forward_loss)).to(self.device) mask = (mask < self.update_proportion).float().to(self.device) forward_loss = (forward_loss * mask).sum() / torch.max( mask.sum(), torch.Tensor([1]).to(self.device)) # --------------------------------------------------------------------------------- traj_info = self.act(s_batch[sample_idx], action_batch[sample_idx], calc_ent=True) ratio = torch.exp(traj_info['log_pi_a'] - log_prob_old_batch[sample_idx]) surr1 = ratio * adv_batch[sample_idx] surr2 = torch.clamp(ratio, 1.0 - self.cliprange, 1.0 + self.cliprange) * adv_batch[sample_idx] policy_loss = -torch.min(surr1, surr2).mean() critic_ext_loss = F.mse_loss(traj_info['v_ext'], target_ext_batch[sample_idx]) critic_int_loss = F.mse_loss(traj_info['v_int'], target_int_batch[sample_idx]) value_loss = critic_ext_loss + critic_int_loss entropy = traj_info['ent'].mean() self.optimizer.zero_grad() loss = policy_loss + 0.5 * value_loss - self.ent_coef * entropy + forward_loss loss.backward() if self.max_grad_norm: nn.utils.clip_grad_norm_( list(self.model.parameters()) + list(self.rnd.predictor.parameters()), self.max_grad_norm) self.optimizer.step() _data = dict(policy=policy_loss.data.cpu().numpy(), value_ext=critic_ext_loss.data.cpu().numpy(), value_int=critic_int_loss.data.cpu().numpy(), entropy=entropy.data.cpu().numpy(), forward=forward_loss.data.cpu().numpy()) for k, v in _data.items(): loss_infos[k].append(v) return loss_infos