def __init__(self, meta_controller_experience_memory=None, lr=0.00025, alpha=0.95, eps=0.01, batch_size=32, gamma=0.99, num_options=12): # expereince replay memory self.meta_controller_experience_memory = meta_controller_experience_memory self.lr = lr # learning rate self.alpha = alpha # optimizer parameter self.eps = 0.01 # optimizer parameter self.gamma = 0.99 # BUILD MODEL USE_CUDA = torch.cuda.is_available() if torch.cuda.is_available() and torch.cuda.device_count() > 1: self.device = torch.device("cuda:1") elif torch.cuda.device_count() == 1: self.device = torch.device("cuda:0") else: self.device = torch.device("cpu") dfloat_cpu = torch.FloatTensor dfloat_gpu = torch.cuda.FloatTensor dlong_cpu = torch.LongTensor dlong_gpu = torch.cuda.LongTensor duint_cpu = torch.ByteTensor dunit_gpu = torch.cuda.ByteTensor dtype = torch.cuda.FloatTensor if torch.cuda.is_available( ) else torch.FloatTensor dlongtype = torch.cuda.LongTensor if torch.cuda.is_available( ) else torch.LongTensor duinttype = torch.cuda.ByteTensor if torch.cuda.is_available( ) else torch.ByteTensor self.dtype = dtype self.dlongtype = dlongtype self.duinttype = duinttype Q = DQN(in_channels=4, num_actions=num_options).type(dtype) Q_t = DQN(in_channels=4, num_actions=num_options).type(dtype) Q_t.load_state_dict(Q.state_dict()) Q_t.eval() for param in Q_t.parameters(): param.requires_grad = False Q = Q.to(self.device) Q_t = Q_t.to(self.device) self.batch_size = batch_size self.Q = Q self.Q_t = Q_t # optimizer optimizer = optim.RMSprop(Q.parameters(), lr=lr, alpha=alpha, eps=eps) self.optimizer = optimizer print('init: Meta Controller --> OK')
class ParallelNashAgent(): def __init__(self, env, id, args): super(ParallelNashAgent, self).__init__() self.id = id self.current_model = DQN(env, args).to(args.device) self.target_model = DQN(env, args).to(args.device) update_target(self.current_model, self.target_model) if args.load_model and os.path.isfile(args.load_model): self.load_model(model_path) self.epsilon_by_frame = epsilon_scheduler(args.eps_start, args.eps_final, args.eps_decay) self.replay_buffer = ParallelReplayBuffer(args.buffer_size) self.rl_optimizer = optim.Adam(self.current_model.parameters(), lr=args.lr) def save_model(self, model_path): torch.save(self.current_model.state_dict(), model_path + f'/{self.id}_dqn') torch.save(self.target_model.state_dict(), model_path + f'/{self.id}_dqn_target') def load_model(self, model_path, eval=False, map_location=None): self.current_model.load_state_dict( torch.load(model_path + f'/{self.id}_dqn', map_location=map_location)) self.target_model.load_state_dict( torch.load(model_path + f'/{self.id}_dqn_target', map_location=map_location)) if eval: self.current_model.eval() self.target_model.eval()
def test(env, args): current_model = DQN(env, args).to(args.device) current_model.eval() load_model(current_model, args) episode_reward = 0 episode_length = 0 state = env.reset() while True: if args.render: env.render() action = current_model.act( torch.FloatTensor(state).to(args.device), 0.) next_state, reward, done, _ = env.step(action) state = next_state episode_reward += reward episode_length += 1 if done: break print("Test Result - Reward {} Length {}".format(episode_reward, episode_length))
def evaluate(env, load_path='agent.pt'): """ Evaluate a trained model and compute your leaderboard scores NO CHANGES SHOULD BE MADE TO THIS FUNCTION Parameters ------- env: gym.Env environment to evaluate on load_path: str path to load the model (.pt) from """ episode_rewards = [0.0] actions = get_action_set() action_size = len(actions) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # These are not the final evaluation seeds, do not overfit on these tracks! seeds = [ 22597174, 68545857, 75568192, 91140053, 86018367, 49636746, 66759182, 91294619, 84274995, 31531469 ] # Build & load network policy_net = DQN(action_size, device).to(device) checkpoint = torch.load(load_path, map_location=device) policy_net.load_state_dict(checkpoint) policy_net.eval() # Iterate over a number of evaluation episodes for i in range(10): env.seed(seeds[i]) obs, done = env.reset(), False obs = get_state(obs) t = 0 # Run each episode until episode has terminated or 600 time steps have been reached while not done and t < 600: env.render() action_id = select_greedy_action(obs, policy_net, action_size) action = actions[action_id] obs, rew, done, _ = env.step(action) obs = get_state(obs) episode_rewards[-1] += rew t += 1 print('episode %d \t reward %f' % (i, episode_rewards[-1])) episode_rewards.append(0.0) print('---------------------------') print(' total score: %f' % np.mean(np.array(episode_rewards))) print('---------------------------')
def train_setting(env, device): init_screen = get_screen(env, device) _, _, screen_height, screen_width = init_screen.shape # Get number of actions from gym action space n_actions = env.action_space.n policy_net = DQN(screen_height, screen_width, n_actions).to(device) target_net = DQN(screen_height, screen_width, n_actions).to(device) target_net.load_state_dict(policy_net.state_dict()) target_net.eval() optimizer = optim.RMSprop(policy_net.parameters()) memory = ReplayMemory(10000) return n_actions, policy_net, target_net, optimizer, memory
def test(env, args): p1_current_model = DQN(env, args).to(args.device) p2_current_model = DQN(env, args).to(args.device) p1_policy = Policy(env).to(args.device) p2_policy = Policy(env).to(args.device) p1_current_model.eval(), p2_current_model.eval() p1_policy.eval(), p2_policy.eval() load_model(models={"p1": p1_current_model, "p2": p2_current_model}, policies={"p1": p1_policy, "p2": p2_policy}, args=args) p1_reward_list = [] p2_reward_list = [] length_list = [] for _ in range(30): (p1_state, p2_state) = env.reset() p1_episode_reward = 0 p2_episode_reward = 0 episode_length = 0 while True: if args.render: env.render() sleep(0.01) # Agents follow average strategy p1_action = p1_policy.act(torch.FloatTensor(p1_state).to(args.device)) p2_action = p2_policy.act(torch.FloatTensor(p2_state).to(args.device)) actions = {"1": p1_action, "2": p2_action} (p1_next_state, p2_next_state), reward, done, _ = env.step(actions) (p1_state, p2_state) = (p1_next_state, p2_next_state) p1_episode_reward += reward[0] p2_episode_reward += reward[1] episode_length += 1 if done: p1_reward_list.append(p1_episode_reward) p2_reward_list.append(p2_episode_reward) length_list.append(episode_length) break print("Test Result - Length {:.2f} p1/Reward {:.2f} p2/Reward {:.2f}".format( np.mean(length_list), np.mean(p1_reward_list), np.mean(p2_reward_list)))
def test(env, args): p1_current_model = DQN(env, args).to(args.device) p2_current_model = DQN(env, args).to(args.device) p1_current_model.eval() p2_current_model.eval() load_model(p1_current_model, args, 1) load_model(p2_current_model, args, 2) p1_reward_list = [] p2_reward_list = [] length_list = [] for _ in range(30): (p1_state, p2_state) = env.reset() p1_episode_reward = 0 p2_episode_reward = 0 episode_length = 0 while True: if args.render: env.render() from time import sleep sleep(0.2) p1_action = p1_current_model.act(torch.FloatTensor(p1_state).to(args.device), 0.0) p2_action = p2_current_model.act(torch.FloatTensor(p2_state).to(args.device), 0.0) actions = {"1": p1_action, "2": p2_action} (p1_next_state, p2_next_state), reward, done, _ = env.step(actions) (p1_state, p2_state) = (p1_next_state, p2_next_state) p1_episode_reward += reward[0] p2_episode_reward += reward[1] episode_length += 1 if done: p1_reward_list.append(p1_episode_reward) p2_reward_list.append(p2_episode_reward) length_list.append(episode_length) break print("Test Result - p1/Reward {} p2/Reward Length {}".format( np.mean(p1_reward_list), np.mean(p2_reward_list)))
def collect_training_rewards(policy_dir_path): env = gym.make("PongDeterministic-v4") epsilon = 0.05 reward_tuples = [] try: for f in os.listdir(policy_dir_path): print(f"processing {f}") dqn = DQN() dqn.load_state_dict( torch.load(policy_dir_path + "/" + f, map_location=torch.device("cpu")) ) dqn.eval() obs = env.reset() s = TrainPongV0.prepare_state(obs) tot_reward = 0 while True: if np.random.rand() < epsilon: a = np.random.choice(range(0, 6)) else: a = dqn(s).argmax() prev_s = s obs, r, d, _ = env.step(a) s = TrainPongV0.prepare_state(obs, prev_s=prev_s) tot_reward += r if d: break reward_tuples.append((tot_reward, int(f.replace("HPC_", "")))) finally: reward_tuples.sort(key=lambda s: s[1]) ls = [s[1] for s in reward_tuples] rs = [s[0] for s in reward_tuples] np.save("reward_tuples", reward_tuples) plt.plot(ls, rs) plt.show() env.close()
def render_model(path, for_gif=False, epsilon=0): """Render model from the given path 0 <= epsilon < 1 """ env = gym.make("PongDeterministic-v4") dqn = DQN() dqn.load_state_dict(torch.load(path, map_location=torch.device("cpu"))) dqn.eval() obs = env.reset() s = TrainPongV0.prepare_state(obs) frames = [] tot_reward = 0 try: for i in range(15000): if for_gif: frames.append(Image.fromarray(env.render(mode="rgb_array"))) else: env.render() if np.random.rand() < epsilon: a = np.random.choice([1, 2, 3]) else: with torch.no_grad(): a = dqn(torch.from_numpy(s))[0].argmax() + 1 prev_s = s obs, r, d, _ = env.step(a) tot_reward += r s = TrainPongV0.prepare_state(obs, prev_s=prev_s) if d: break except KeyboardInterrupt: pass env.close() if for_gif: return tot_reward, frames
def test(env, args): current_model = DQN(env, args).to(args.device) current_model.eval() load_model(current_model, args) episode_reward = 0 episode_length = 0 state_buffer = deque(maxlen=args.action_repeat) states_deque = actions_deque = rewards_deque = None state, state_buffer = get_initial_state(env, state_buffer, args.action_repeat) while True: action = current_model.act(torch.FloatTensor(state).to(args.device), 0.) next_state, _, done, end = env.step(action, save_screenshots=True) add_state(next_state, state_buffer) next_state = recent_state(state_buffer) state = next_state if end: break # delete the agents that have reached the goal r_index = 0 for r in range(len(done)): if done[r] is True: state_buffer, states_deque, actions_deque, rewards_deque = \ del_record(r_index, state_buffer, states_deque, actions_deque, rewards_deque) r_index -= 1 r_index += 1 next_state = recent_state(state_buffer) state = next_state PanicEnv.display(True) print("Test Result - Reward {} Length {}".format(episode_reward, episode_length))
class AgentEval: def __init__(self, args, env): self.action_space = env.action_space self.atoms = args.atoms self.v_min = args.V_min self.v_max = args.V_max self.support = torch.linspace(args.V_min, args.V_max, self.atoms).to( device=args.device) # Support (range) of z self.delta_z = (args.V_max - args.V_min) / (self.atoms - 1) self.online_net = DQN(args, self.action_space).to(device=args.device) for m in self.online_net.modules(): print(m) if args.model and os.path.isfile(args.model): # Always load tensors onto CPU by default, will shift to GPU if necessary self.online_net.load_state_dict(torch.load(args.model, map_location='cpu')) self.online_net.eval() # Resets noisy weights in all linear layers (of online net only) def reset_noise(self): self.online_net.reset_noise() # Acts based on single state (no batch) def act(self, state): with torch.no_grad(): return (self.online_net(state.unsqueeze(0)) * self.support).sum(2).argmax(1).item() # Acts with an ε-greedy policy (used for evaluation only) def act_e_greedy(self, state, epsilon=0.001): # High ε can reduce evaluation scores drastically return self.action_space.sample() if np.random.random() < epsilon else self.act(state) # Evaluates Q-value based on single state (no batch) def evaluate_q(self, state): with torch.no_grad(): return (self.online_net(state.unsqueeze(0)) * self.support).sum(2).max(1)[0].item()
if torch.cuda.is_available(): device0 = torch.device("cuda:0") else: device0 = torch.device("cpu") dtype = torch.cuda.FloatTensor if torch.cuda.is_available( ) else torch.FloatTensor dlongtype = torch.cuda.LongTensor if torch.cuda.is_available( ) else torch.LongTensor duinttype = torch.cuda.ByteTensor if torch.cuda.is_available( ) else torch.ByteTensor Qt = DQN(in_channels=5, num_actions=18).type(dtype) Qt_t = DQN(in_channels=5, num_actions=18).type(dtype) Qt_t.load_state_dict(Qt.state_dict()) Qt_t.eval() for param in Qt_t.parameters(): param.requires_grad = False if torch.cuda.device_count() > 0: Qt = nn.DataParallel(Qt).to(device0) Qt_t = nn.DataParallel(Qt_t).to(device0) batch_size = BATCH_SIZE * torch.cuda.device_count() else: batch_size = BATCH_SIZE # optimizer optimizer = optim.RMSprop(Qt.parameters(), lr=LEARNING_RATE, alpha=ALPHA, eps=EPS)
class DQNAgent: def __init__(self, state_size, action_size, config=RLConfig()): self.seed = random.seed(config.seed) self.state_size = state_size self.action_size = action_size self.batch_size = config.batch_size self.batch_indices = torch.arange(config.batch_size).long().to(device) self.samples_before_learning = config.samples_before_learning self.learn_interval = config.learning_interval self.parameter_update_interval = config.parameter_update_interval self.per_epsilon = config.per_epsilon self.tau = config.tau self.gamma = config.gamma if config.useDuelingDQN: self.qnetwork_local = DuelingDQN(state_size, action_size, config.seed).to(device) self.qnetwork_target = DuelingDQN(state_size, action_size, config.seed).to(device) else: self.qnetwork_local = DQN(state_size, action_size, config.seed).to(device) self.qnetwork_target = DQN(state_size, action_size, config.seed).to(device) self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=config.learning_rate) self.doubleDQN = config.useDoubleDQN self.usePER = config.usePER if self.usePER: self.memory = PrioritizedReplayBuffer(config.buffer_size, config.per_alpha) else: self.memory = ReplayBuffer(config.buffer_size) self.t_step = 0 def act(self, state, eps=0.): state = torch.from_numpy(state).float().unsqueeze(0).to(device) self.qnetwork_local.eval() with torch.no_grad(): action_values = self.qnetwork_local(state) self.qnetwork_local.train() if random.random() < eps: return random.choice(np.arange(self.action_size)) else: return np.argmax(action_values.cpu().data.numpy()) def step(self, state, action, reward, next_state, done, beta): self.memory.add(state, action, reward, next_state, done) self.t_step += 1 if self.t_step % self.learn_interval == 0: if len(self.memory) > self.samples_before_learning: state = torch.from_numpy(state).float().unsqueeze(0).to(device) next_state = torch.from_numpy(next_state).float().unsqueeze( 0).to(device) target = self.qnetwork_local(state).data old_val = target[0][action] target_val = self.qnetwork_target(next_state).data if done: target[0][action] = reward else: target[0][ action] = reward + self.gamma * torch.max(target_val) if self.usePER: states, actions, rewards, next_states, dones, weights, indices = self.memory.sample( self.batch_size, beta) else: indices = None weights = None states, actions, rewards, next_states, dones = self.memory.sample( self.batch_size) self.learn(states, actions, rewards, next_states, dones, indices, weights, self.gamma) def learn(self, states, actions, rewards, next_states, dones, indices, weights, gamma): states = torch.from_numpy(np.vstack(states)).float().to(device) actions = torch.from_numpy(np.vstack(actions)).long().to(device) rewards = torch.from_numpy(np.vstack(rewards)).float().to(device) next_states = torch.from_numpy( np.vstack(next_states)).float().to(device) dones = torch.from_numpy(np.vstack(dones.astype( np.uint8))).float().to(device) Q_targets_next = self.qnetwork_target(next_states).detach() if self.doubleDQN: # choose the best action from the local network next_actions = self.qnetwork_local(next_states).argmax(dim=-1) Q_targets_next = Q_targets_next[self.batch_indices, next_actions] else: Q_targets_next = Q_targets_next.max(1)[0] Q_targets = rewards + gamma * Q_targets_next.reshape( (self.batch_size, 1)) * (1 - dones) pred = self.qnetwork_local(states) Q_expected = pred.gather(1, actions) if self.usePER: errors = torch.abs(Q_expected - Q_targets).data.numpy() + self.per_epsilon self.memory.update_priorities(indices, errors) self.optimizer.zero_grad() loss = F.mse_loss(Q_expected, Q_targets) loss.backward() self.optimizer.step() if self.t_step % self.parameter_update_interval == 0: self.soft_update(self.qnetwork_local, self.qnetwork_target, self.tau) def soft_update(self, qnetwork_local, qnetwork_target, tau): for local_param, target_param in zip(qnetwork_local.parameters(), qnetwork_target.parameters()): target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data)
# ------------------------------------------------------------------------------ DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") DEVICE = "cpu" print(f"Using device: {DEVICE}") print(f"Settings:\n{SETTINGS}") n_actions = len(SETTINGS["actions"]) n_episodes = SETTINGS["num_episodes"] max_episode_len = SETTINGS["max_episode_length"] dims = SETTINGS["world_dims"] eps = SETTINGS["eps"] policy_net = DQN(dims[0], dims[1], dims[2], n_actions).to(DEVICE) target_net = DQN(dims[0], dims[1], dims[2], n_actions).to(DEVICE) target_net.load_state_dict(policy_net.state_dict()) target_net.eval() optimizer = optim.RMSprop(policy_net.parameters()) memory = ExperienceReplay(100) total_steps = 0 env = gameEnv(partial=False, size=SETTINGS["world_size"]) # # Methods # ------------------------------------------------------------------------------ def select_action(state, eps, n_actions): f"""
class Agent(): def __init__(self, args, env): self.args = args self.action_space = env.action_space() self.atoms = args.atoms self.Vmin = args.V_min self.Vmax = args.V_max self.support = torch.linspace(args.V_min, args.V_max, self.atoms).to( device=args.device) # Support (range) of z self.delta_z = (args.V_max - args.V_min) / (self.atoms - 1) self.batch_size = args.batch_size self.n = args.multi_step self.discount = args.discount self.norm_clip = args.norm_clip self.coeff = 0.01 if args.game in [ 'pong', 'boxing', 'private_eye', 'freeway' ] else 1. self.online_net = DQN(args, self.action_space).to(device=args.device) self.momentum_net = DQN(args, self.action_space).to(device=args.device) # self.predictor = prediction_MLP(in_dim=128, hidden_dim=128, out_dim=128) if args.model: # Load pretrained model if provided if os.path.isfile(args.model): state_dict = torch.load( args.model, map_location='cpu' ) # Always load tensors onto CPU by default, will shift to GPU if necessary if 'conv1.weight' in state_dict.keys(): for old_key, new_key in (('conv1.weight', 'convs.0.weight'), ('conv1.bias', 'convs.0.bias'), ('conv2.weight', 'convs.2.weight'), ('conv2.bias', 'convs.2.bias'), ('conv3.weight', 'convs.4.weight'), ('conv3.bias', 'convs.4.bias')): state_dict[new_key] = state_dict[ old_key] # Re-map state dict for old pretrained models del state_dict[ old_key] # Delete old keys for strict load_state_dict self.online_net.load_state_dict(state_dict) print("Loading pretrained model: " + args.model) else: # Raise error if incorrect model path provided raise FileNotFoundError(args.model) self.online_net.train() # self.pred.train() self.initialize_momentum_net() self.momentum_net.train() self.target_net = DQN(args, self.action_space).to(device=args.device) self.update_target_net() self.target_net.train() for param in self.target_net.parameters(): param.requires_grad = False for param in self.momentum_net.parameters(): param.requires_grad = False self.optimiser = optim.Adam(self.online_net.parameters(), lr=args.learning_rate, eps=args.adam_eps) # Resets noisy weights in all linear layers (of online net only) def reset_noise(self): self.online_net.reset_noise() # Acts based on single state (no batch) def act(self, state): with torch.no_grad(): a, _, _ = self.online_net(state.unsqueeze(0)) return (a * self.support).sum(2).argmax(1).item() # Acts with an ε-greedy policy (used for evaluation only) def act_e_greedy( self, state, epsilon=0.001): # High ε can reduce evaluation scores drastically return np.random.randint( 0, self.action_space ) if np.random.random() < epsilon else self.act(state) def learn(self, mem): # Sample transitions idxs, states, actions, returns, next_states, nonterminals, weights = mem.sample( self.batch_size) # print('\n\n---------------') # print(f'idxs: {idxs}, ') # print(f'states: {states.shape}, ') # print(f'actions: {actions.shape}, ') # print(f'returns: {returns.shape}, ') # print(f'next_states: {next_states.shape}, ') # print(f'nonterminals: {nonterminals.shape}, ') # print(f'weights: {weights.shape},') aug_states_1 = aug(states).to(device=self.args.device) aug_states_2 = aug(states).to(device=self.args.device) # print(f'aug_states_1: {aug_states_1.shape}') # print(f'aug_states_2: {aug_states_2.shape}') # Calculate current state probabilities (online network noise already sampled) log_ps, _, _ = self.online_net( states, log=True) # Log probabilities log p(s_t, ·; θonline) _, z_1, p_1 = self.online_net(aug_states_1, log=True) _, z_2, p_2 = self.online_net(aug_states_2, log=True) # p_1, p_2 = self.pred(z_1), self.pred(z_2) # with torch.no_grad(): # p_2 = self.pred(z_2) simsiam_loss = 2 + D(p_1, z_2) / 2 + D(p_2, z_1) / 2 # simsiam_loss = p_1.mean() + p_2.mean() # simsiam_loss = p_1.mean() * 128 # simsiam_loss = - F.cosine_similarity(p_1, z_2.detach(), dim=-1).mean() # print(simsiam_loss) # simsiam_loss = 0 # _, z_target = self.momentum_net(aug_states_2, log=True) #z_k # z_proj = torch.matmul(self.online_net.W, z_target.T) # logits = torch.matmul(z_anch, z_proj) # logits = (logits - torch.max(logits, 1)[0][:, None]) # logits = logits * 0.1 # labels = torch.arange(logits.shape[0]).long().to(device=self.args.device) # moco_loss = (nn.CrossEntropyLoss()(logits, labels)).to(device=self.args.device) log_ps_a = log_ps[range(self.batch_size), actions] # log p(s_t, a_t; θonline) # print(f'z_1: {z_1.shape}') # print(f'p_1: {p_1.shape}') # print('---------------\n\n') # 1/0 with torch.no_grad(): # Calculate nth next state probabilities pns, _, _ = self.online_net( next_states) # Probabilities p(s_t+n, ·; θonline) dns = self.support.expand_as( pns) * pns # Distribution d_t+n = (z, p(s_t+n, ·; θonline)) argmax_indices_ns = dns.sum(2).argmax( 1 ) # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))] self.target_net.reset_noise() # Sample new target net noise pns, _, _ = self.target_net( next_states) # Probabilities p(s_t+n, ·; θtarget) pns_a = pns[range( self.batch_size ), argmax_indices_ns] # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget) # Compute Tz (Bellman operator T applied to z) Tz = returns.unsqueeze(1) + nonterminals * ( self.discount**self.n) * self.support.unsqueeze( 0) # Tz = R^n + (γ^n)z (accounting for terminal states) Tz = Tz.clamp(min=self.Vmin, max=self.Vmax) # Clamp between supported values # Compute L2 projection of Tz onto fixed support z b = (Tz - self.Vmin) / self.delta_z # b = (Tz - Vmin) / Δz l, u = b.floor().to(torch.int64), b.ceil().to(torch.int64) # Fix disappearing probability mass when l = b = u (b is int) l[(u > 0) * (l == u)] -= 1 u[(l < (self.atoms - 1)) * (l == u)] += 1 # Distribute probability of Tz m = states.new_zeros(self.batch_size, self.atoms) offset = torch.linspace(0, ((self.batch_size - 1) * self.atoms), self.batch_size).unsqueeze(1).expand( self.batch_size, self.atoms).to(actions) m.view(-1).index_add_( 0, (l + offset).view(-1), (pns_a * (u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b) m.view(-1).index_add_( 0, (u + offset).view(-1), (pns_a * (b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l) loss = -torch.sum( m * log_ps_a, 1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t))) # loss = loss + (moco_loss * self.coeff) loss = loss + (simsiam_loss * self.coeff) self.online_net.zero_grad() # self.pred.zero_grad() curl_loss = (weights * loss).mean() # print(curl_loss) curl_loss.mean().backward( ) # Backpropagate importance-weighted minibatch loss clip_grad_norm_(self.online_net.parameters(), self.norm_clip) # Clip gradients by L2 norm self.optimiser.step() mem.update_priorities(idxs, loss.detach().cpu().numpy() ) # Update priorities of sampled transitions def learn_old(self, mem): # Sample transitions idxs, states, actions, returns, next_states, nonterminals, weights = mem.sample( self.batch_size) # print('\n\n---------------') # print(f'idxs: {idxs}, ') # print(f'states: {states.shape}, ') # print(f'actions: {actions.shape}, ') # print(f'returns: {returns.shape}, ') # print(f'next_states: {next_states.shape}, ') # print(f'nonterminals: {nonterminals.shape}, ') # print(f'weights: {weights.shape},') aug_states_1 = aug(states).to(device=self.args.device) aug_states_2 = aug(states).to(device=self.args.device) # print(f'aug_states_1: {aug_states_1.shape}') # print(f'aug_states_2: {aug_states_2.shape}') # Calculate current state probabilities (online network noise already sampled) log_ps, _, _ = self.online_net( states, log=True) # Log probabilities log p(s_t, ·; θonline) _, z_anch, _ = self.online_net(aug_states_1, log=True) #z_q _, z_target, _ = self.momentum_net(aug_states_2, log=True) #z_k z_proj = torch.matmul(self.online_net.W, z_target.T) logits = torch.matmul(z_anch, z_proj) logits = (logits - torch.max(logits, 1)[0][:, None]) logits = logits * 0.1 labels = torch.arange( logits.shape[0]).long().to(device=self.args.device) moco_loss = (nn.CrossEntropyLoss()(logits, labels)).to(device=self.args.device) log_ps_a = log_ps[range(self.batch_size), actions] # log p(s_t, a_t; θonline) # print(f'z_anch: {z_anch.shape}') # print(f'z_target: {z_target.shape}') # print(f'z_proj: {z_proj.shape}') # print(f'logits: {logits.shape}') # print(logits) # print(f'labels: {labels.shape}') # print(labels) # print('---------------\n\n') # 1/0 with torch.no_grad(): # Calculate nth next state probabilities pns, _, _ = self.online_net( next_states) # Probabilities p(s_t+n, ·; θonline) dns = self.support.expand_as( pns) * pns # Distribution d_t+n = (z, p(s_t+n, ·; θonline)) argmax_indices_ns = dns.sum(2).argmax( 1 ) # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))] self.target_net.reset_noise() # Sample new target net noise pns, _, _ = self.target_net( next_states) # Probabilities p(s_t+n, ·; θtarget) pns_a = pns[range( self.batch_size ), argmax_indices_ns] # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget) # Compute Tz (Bellman operator T applied to z) Tz = returns.unsqueeze(1) + nonterminals * ( self.discount**self.n) * self.support.unsqueeze( 0) # Tz = R^n + (γ^n)z (accounting for terminal states) Tz = Tz.clamp(min=self.Vmin, max=self.Vmax) # Clamp between supported values # Compute L2 projection of Tz onto fixed support z b = (Tz - self.Vmin) / self.delta_z # b = (Tz - Vmin) / Δz l, u = b.floor().to(torch.int64), b.ceil().to(torch.int64) # Fix disappearing probability mass when l = b = u (b is int) l[(u > 0) * (l == u)] -= 1 u[(l < (self.atoms - 1)) * (l == u)] += 1 # Distribute probability of Tz m = states.new_zeros(self.batch_size, self.atoms) offset = torch.linspace(0, ((self.batch_size - 1) * self.atoms), self.batch_size).unsqueeze(1).expand( self.batch_size, self.atoms).to(actions) m.view(-1).index_add_( 0, (l + offset).view(-1), (pns_a * (u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b) m.view(-1).index_add_( 0, (u + offset).view(-1), (pns_a * (b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l) loss = -torch.sum( m * log_ps_a, 1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t))) print(moco_loss) loss = loss + (moco_loss * self.coeff) self.online_net.zero_grad() curl_loss = (weights * loss).mean() curl_loss.mean().backward( ) # Backpropagate importance-weighted minibatch loss clip_grad_norm_(self.online_net.parameters(), self.norm_clip) # Clip gradients by L2 norm self.optimiser.step() mem.update_priorities(idxs, loss.detach().cpu().numpy() ) # Update priorities of sampled transitions def update_target_net(self): self.target_net.load_state_dict(self.online_net.state_dict()) def initialize_momentum_net(self): for param_q, param_k in zip(self.online_net.parameters(), self.momentum_net.parameters()): param_k.data.copy_(param_q.data) # update param_k.requires_grad = False # not update by gradient # Code for this function from https://github.com/facebookresearch/moco @torch.no_grad() def update_momentum_net(self, momentum=0.999): for param_q, param_k in zip(self.online_net.parameters(), self.momentum_net.parameters()): param_k.data.copy_(momentum * param_k.data + (1. - momentum) * param_q.data) # update # Save model parameters on current device (don't move model between devices) def save(self, path, name='model.pth'): torch.save(self.online_net.state_dict(), os.path.join(path, name)) # Evaluates Q-value based on single state (no batch) def evaluate_q(self, state): with torch.no_grad(): a, _, _ = self.online_net(state.unsqueeze(0)) return (a * self.support).sum(2).max(1)[0].item() def train(self): self.online_net.train() def eval(self): self.online_net.eval()
class Agent(): def __init__(self, args, env): self.action_space = env.action_space() self.batch_size = args.batch_size self.discount = args.discount self.max_gradient_norm = args.max_gradient_norm self.policy_net = DQN(args, self.action_space) if args.model and os.path.isfile(args.model): self.policy_net.load_state_dict(torch.load(args.model)) self.policy_net.train() self.target_net = DQN(args, self.action_space) self.update_target_net() self.target_net.eval() self.optimiser = optim.Adam(self.policy_net.parameters(), lr=args.lr) def act(self, state, epsilon): if random.random() > epsilon: return self.policy_net(state.unsqueeze(0)).max(1)[1].data[0] else: return random.randint(0, self.action_space - 1) def learn(self, mem): transitions = mem.sample(self.batch_size) batch = Transition(*zip(*transitions)) # Transpose the batch states = Variable(torch.stack(batch.state, 0)) actions = Variable(torch.LongTensor(batch.action).unsqueeze(1)) rewards = Variable(torch.Tensor(batch.reward)) non_final_mask = torch.ByteTensor( tuple(map( lambda s: s is not None, batch.next_state))) # Only process non-terminal next states next_states = Variable( torch.stack(tuple(s for s in batch.next_state if s is not None), 0), volatile=True ) # Prevent backpropagating through expected action values Qs = self.policy_net(states).gather(1, actions) # Q(s_t, a_t; θpolicy) next_state_argmax_indices = self.policy_net(next_states).max( 1, keepdim=True )[1] # Perform argmax action selection using policy network: argmax_a[Q(s_t+1, a; θpolicy)] Qns = Variable(torch.zeros( self.batch_size)) # Q(s_t+1, a) = 0 if s_t+1 is terminal Qns[non_final_mask] = self.target_net(next_states).gather( 1, next_state_argmax_indices ) # Q(s_t+1, argmax_a[Q(s_t+1, a; θpolicy)]; θtarget) Qns.volatile = False # Remove volatile flag to prevent propagating it through loss target = rewards + ( self.discount * Qns ) # Double-Q target: Y = r + γ.Q(s_t+1, argmax_a[Q(s_t+1, a; θpolicy)]; θtarget) loss = F.smooth_l1_loss( Qs, target) # Huber loss on TD-error δ: δ = Y - Q(s_t, a_t) # TODO: TD-error clipping? self.policy_net.zero_grad() loss.backward() nn.utils.clip_grad_norm(self.policy_net.parameters(), self.max_gradient_norm) # Clamp gradients self.optimiser.step() def update_target_net(self): self.target_net.load_state_dict(self.policy_net.state_dict()) def save(self, path): torch.save(self.policy_net.state_dict(), os.path.join(path, 'model.pth')) def evaluate_q(self, state): return self.policy_net(state.unsqueeze(0)).max(1)[0].data[0] def train(self): self.policy_net.train() def eval(self): self.policy_net.eval()
class Agent(): """Interacts with and learns from the environment.""" def __init__(self, state_size, action_size, seed, gamma=0.99, step_size=1, dueling_dqn=False): """Initialize an Agent object. Params ====== state_size (int): dimension of each state action_size (int): dimension of each action seed (int): random seed """ self.state_size = state_size self.action_size = action_size self.seed = random.seed(seed) # Q-Network if dueling_dqn: print("Use dueling dqn") self.qnetwork_local = NoisyDuelingDQN(state_size, action_size, seed).to(device) self.qnetwork_target = NoisyDuelingDQN(state_size, action_size, seed).to(device) else: print("Use non-dueling dqn") self.qnetwork_local = DQN(state_size, action_size, seed).to(device) self.qnetwork_target = DQN(state_size, action_size, seed).to(device) self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR) # Replay memory self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed) # Initialize time step (for updating every UPDATE_EVERY steps) self.t_step = 0 self.gamma = gamma self.step_size = step_size def step(self, state, action, reward, next_state, done): # Save experience in replay memory self.memory.add(state, action, reward, next_state, done) # Learn every UPDATE_EVERY time steps. self.t_step = (self.t_step + 1) % UPDATE_EVERY if self.t_step == 0: # If enough samples are available in memory, get random subset and learn if len(self.memory) > BATCH_SIZE: experiences = self.memory.sample() self.learn(experiences) def act(self, state): """Returns actions for given state as per current policy. Params ====== state (array_like): current state """ state = torch.from_numpy(state).float().unsqueeze(0).to(device) self.qnetwork_local.eval() with torch.no_grad(): action_values = self.qnetwork_local(state) self.qnetwork_local.train() return np.argmax(action_values.cpu().data.numpy()) def learn(self, experiences): """Update value parameters using given batch of experience tuples. Params ====== experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples gamma (float): discount factor """ states, actions, rewards, next_states, dones = experiences # Compute and minimize loss # Get max predicted Q values (for next states) from target model Q_targets_next = self.qnetwork_target(next_states).detach().max( 1)[0].unsqueeze(1) # Compute Q targets for current states ## gamma ^ step_size for nstep dqn Q_targets = rewards + (pow(self.gamma, self.step_size) * Q_targets_next * (1 - dones)) # Get expected Q values from local model Q_expected = self.qnetwork_local(states).gather(1, actions) # Compute loss loss = F.mse_loss(Q_expected, Q_targets) # Minimize the loss self.optimizer.zero_grad() loss.backward() self.optimizer.step() # ------------------- update target network ------------------- # self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU) def soft_update(self, local_model, target_model, tau): """Soft update model parameters. θ_target = τ*θ_local + (1 - τ)*θ_target Params ====== local_model (PyTorch model): weights will be copied from target_model (PyTorch model): weights will be copied to tau (float): interpolation parameter """ for target_param, local_param in zip(target_model.parameters(), local_model.parameters()): target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data)
class Agent(): def __init__(self, args, env): self.action_space = env.action_space() self.atoms = args.atoms self.Vmin = args.V_min self.Vmax = args.V_max self.support = torch.linspace(args.V_min, args.V_max, self.atoms).to( device=args.device) # Support (range) of z self.delta_z = (args.V_max - args.V_min) / (self.atoms - 1) self.batch_size = args.batch_size self.n = args.multi_step self.discount = args.discount self.online_net = DQN(args, self.action_space).to(device=args.device) if args.model and os.path.isfile(args.model): # Always load tensors onto CPU by default, will shift to GPU if necessary self.online_net.load_state_dict( torch.load(args.model, map_location='cpu')) self.online_net.train() self.target_net = DQN(args, self.action_space).to(device=args.device) self.update_target_net() self.target_net.train() for param in self.target_net.parameters(): param.requires_grad = False self.optimiser = optim.Adam(self.online_net.parameters(), lr=args.lr, eps=args.adam_eps) # Resets noisy weights in all linear layers (of online net only) def reset_noise(self): self.online_net.reset_noise() # Acts based on single state (no batch) def act(self, state): with torch.no_grad(): return (self.online_net(state.unsqueeze(0)) * self.support).sum(2).argmax(1).item() # Acts with an ε-greedy policy (used for evaluation only) def act_e_greedy( self, state, epsilon=0.001): # High ε can reduce evaluation scores drastically return random.randrange( self.action_space) if random.random() < epsilon else self.act( state) def learn(self, mem): # Sample transitions idxs, states, actions, returns, next_states, nonterminals, weights = mem.sample( self.batch_size) # Calculate current state probabilities (online network noise already sampled) log_ps = self.online_net( states, log=True) # Log probabilities log p(s_t, ·; θonline) log_ps_a = log_ps[range(self.batch_size), actions] # log p(s_t, a_t; θonline) with torch.no_grad(): # Calculate nth next state probabilities pns = self.online_net( next_states) # Probabilities p(s_t+n, ·; θonline) dns = self.support.expand_as( pns) * pns # Distribution d_t+n = (z, p(s_t+n, ·; θonline)) argmax_indices_ns = dns.sum(2).argmax( 1 ) # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))] self.target_net.reset_noise() # Sample new target net noise pns = self.target_net( next_states) # Probabilities p(s_t+n, ·; θtarget) pns_a = pns[range( self.batch_size ), argmax_indices_ns] # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget) # Compute Tz (Bellman operator T applied to z) Tz = returns.unsqueeze(1) + nonterminals * ( self.discount**self.n) * self.support.unsqueeze( 0) # Tz = R^n + (γ^n)z (accounting for terminal states) Tz = Tz.clamp(min=self.Vmin, max=self.Vmax) # Clamp between supported values # Compute L2 projection of Tz onto fixed support z b = (Tz - self.Vmin) / self.delta_z # b = (Tz - Vmin) / Δz l, u = b.floor().to(torch.int64), b.ceil().to(torch.int64) # Fix disappearing probability mass when l = b = u (b is int) l[(u > 0) * (l == u)] -= 1 u[(l < (self.atoms - 1)) * (l == u)] += 1 # Distribute probability of Tz m = states.new_zeros(self.batch_size, self.atoms) offset = torch.linspace(0, ((self.batch_size - 1) * self.atoms), self.batch_size).unsqueeze(1).expand( self.batch_size, self.atoms).to(actions) m.view(-1).index_add_( 0, (l + offset).view(-1), (pns_a * (u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b) m.view(-1).index_add_( 0, (u + offset).view(-1), (pns_a * (b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l) loss = -torch.sum( m * log_ps_a, 1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t))) self.online_net.zero_grad() (weights * loss).mean().backward( ) # Backpropagate importance-weighted minibatch loss self.optimiser.step() mem.update_priorities( idxs, loss.detach()) # Update priorities of sampled transitions def update_target_net(self): self.target_net.load_state_dict(self.online_net.state_dict()) # Save model parameters on current device (don't move model between devices) def save(self, path): torch.save(self.online_net.state_dict(), os.path.join(path, 'model.pth')) # Evaluates Q-value based on single state (no batch) def evaluate_q(self, state): with torch.no_grad(): return (self.online_net(state.unsqueeze(0)) * self.support).sum(2).max(1)[0].item() def train(self): self.online_net.train() def eval(self): self.online_net.eval()
optimizer.zero_grad() loss.backward() optimizer.step() # Do the soft target update paramlist = list() for i, param in enumerate(model.parameters()): paramlist.append(param) for i, tparam in enumerate(target.parameters()): tparam.data.copy_(tau * paramlist[i].data + (1 - tau) * tparam.data) # Handle epsilon-greedy exploration state = torch.from_numpy(state).float().unsqueeze(0) model.eval() with torch.no_grad(): Qsa = model(state) model.train() # Handle exploration/exploitation rand = random.uniform(0, 1) if rand < epsilon: # Explore action = random.choice(np.arange(total_actions)) #TODO: change else: # Exploit action = np.argmax(Qsa.data.numpy()) # Get the next state next_state, reward, done, info = env.step(action) score += reward
class Agent: """ The intelligent agent of the simulation. Set the model of the neural network used and general parameters. It is responsible to select the actions, optimize the neural network and manage the models. """ def __init__(self, action_set, train=True, load_path=None): #1. Initialize agent params self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.action_set = action_set self.action_number = len(action_set) self.steps_done = 0 self.epsilon = Config.EPS_START self.episode_durations = [] print('LOAD PATH -- agent.init:', load_path) time.sleep(2) #2. Build networks self.policy_net = DQN().to(self.device) self.target_net = DQN().to(self.device) self.optimizer = optim.RMSprop(self.policy_net.parameters(), lr=Config.LEARNING_RATE) if not train: print('entrou no not train') self.optimizer = optim.RMSprop(self.policy_net.parameters(), lr=0) self.policy_net.load(load_path, optimizer=self.optimizer) self.policy_net.eval() self.target_net.load_state_dict(self.policy_net.state_dict()) self.target_net.eval() self.memory = ReplayMemory(1000) def select_action(self, state, train=True): """ Selet the best action according to the Q-values outputed from the neural network Parameters ---------- state: float ndarray The current state on the simulation train: bool Define if we are evaluating or trainning the model Returns ------- a.max(1)[1]: int The action with the highest Q-value a.max(0): float The Q-value of the action taken """ global steps_done sample = random.random() #1. Perform a epsilon-greedy algorithm #a. set the value for epsilon self.epsilon = Config.EPS_END + (Config.EPS_START - Config.EPS_END) * \ math.exp(-1. * self.steps_done / Config.EPS_DECAY) self.steps_done += 1 #b. make the decision for selecting a random action or selecting an action from the neural network if sample > self.epsilon or (not train): # select an action from the neural network with torch.no_grad(): # a <- argmax Q(s, theta) a = self.policy_net(state) return a.max(1)[1].view(1, 1), a.max(0) else: # select a random action print('random action') return torch.tensor([[random.randrange(2)]], device=self.device, dtype=torch.long), None def optimize_model(self): """ Perform one step of optimization on the neural network """ if len(self.memory) < Config.BATCH_SIZE: return transitions = self.memory.sample(Config.BATCH_SIZE) # Transpose the batch (see http://stackoverflow.com/a/19343/3343043 for detailed explanation). batch = Transition(*zip(*transitions)) # Compute a mask of non-final states and concatenate the batch elements non_final_mask = torch.tensor(tuple(map(lambda s: s is not None, batch.next_state)), device=self.device, dtype=torch.uint8) non_final_next_states = torch.cat([s for s in batch.next_state if s is not None]) state_batch = torch.cat(batch.state) action_batch = torch.cat(batch.action) reward_batch = torch.cat(batch.reward) # Compute Q(s_t, a) - the model computes Q(s_t), then we select the columns of actions taken state_action_values = self.policy_net(state_batch).gather(1, action_batch) # Compute argmax Q(s', a; θ) next_state_actions = self.policy_net(non_final_next_states).max(1)[1].detach().unsqueeze(1) # Compute Q(s', argmax Q(s', a; θ), θ-) next_state_values = torch.zeros(Config.BATCH_SIZE, device=self.device) next_state_values[non_final_mask] = self.target_net(non_final_next_states).gather(1, next_state_actions).squeeze(1).detach() # Compute the expected Q values expected_state_action_values = (next_state_values * Config.GAMMA) + reward_batch # Compute Huber loss loss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1)) # Optimize the model self.optimizer.zero_grad() loss.backward() for param in self.policy_net.parameters(): param.grad.data.clamp_(-1, 1) self.optimizer.step() def save(self, step, logs_path, label): """ Save the model on hard disc Parameters ---------- step: int current step on the simulation logs_path: string path to where we will store the model label: string label that will be used to store the model """ os.makedirs(logs_path + label, exist_ok=True) full_label = label + str(step) + '.pth' logs_path = os.path.join(logs_path, label, full_label) self.policy_net.save(logs_path, step=step, optimizer=self.optimizer) def restore(self, logs_path): """ Load the model from hard disc Parameters ---------- logs_path: string path to where we will store the model """ self.policy_net.load(logs_path) self.target_net.load(logs_path)
class Agent(): def __init__(self, args, env): self.action_space = env.action_space() self.atoms = args.atoms self.Vmin = args.V_min self.Vmax = args.V_max self.support = torch.linspace(args.V_min, args.V_max, args.atoms) # Support (range) of z self.delta_z = (args.V_max - args.V_min) / (args.atoms - 1) self.batch_size = args.batch_size self.n = args.multi_step self.discount = args.discount self.priority_exponent = args.priority_exponent self.max_gradient_norm = args.max_gradient_norm self.policy_net = DQN(args, self.action_space) if args.model and os.path.isfile(args.model): self.policy_net.load_state_dict(torch.load(args.model)) self.policy_net.train() self.target_net = DQN(args, self.action_space) self.update_target_net() self.target_net.eval() self.optimiser = optim.Adam(self.policy_net.parameters(), lr=args.lr, eps=args.adam_eps) if args.cuda: self.policy_net.cuda() self.target_net.cuda() self.support = self.support.cuda() # Resets noisy weights in all linear layers (of policy and target nets) def reset_noise(self): self.policy_net.reset_noise() self.target_net.reset_noise() # Acts based on single state (no batch) def act(self, state): return (self.policy_net(state.unsqueeze(0)).data * self.support).sum(2).max(1)[1][0] def learn(self, mem): idxs, states, actions, returns, next_states, nonterminals, weights = mem.sample(self.batch_size) batch_size = len(idxs) # May return less than specified if invalid transitions sampled # Calculate current state probabilities ps = self.policy_net(states) # Probabilities p(s_t, ·; θpolicy) ps_a = ps[range(batch_size), actions] # p(s_t, a_t; θpolicy) # Calculate nth next state probabilities pns = self.policy_net(next_states).data # Probabilities p(s_t+n, ·; θpolicy) dns = self.support.expand_as(pns) * pns # Distribution d_t+n = (z, p(s_t+n, ·; θpolicy)) argmax_indices_ns = dns.sum(2).max(1)[1] # Perform argmax action selection using policy network: argmax_a[(z, p(s_t+n, a; θpolicy))] pns = self.target_net(next_states).data # Probabilities p(s_t+n, ·; θtarget) pns_a = pns[range(batch_size), argmax_indices_ns] # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θpolicy))]; θtarget) pns_a *= nonterminals # Set p = 0 for terminal nth next states as all possible expected returns = expected reward at final transition # Compute Tz (Bellman operator T applied to z) Tz = returns.unsqueeze(1) + nonterminals * (self.discount ** self.n) * self.support.unsqueeze(0) # Tz = R^n + (γ^n)z (accounting for terminal states) Tz = Tz.clamp(min=self.Vmin, max=self.Vmax) # Clamp between supported values # Compute L2 projection of Tz onto fixed support z b = (Tz - self.Vmin) / self.delta_z # b = (Tz - Vmin) / Δz l, u = b.floor().long(), b.ceil().long() # Distribute probability of Tz m = states.data.new(batch_size, self.atoms).zero_() offset = torch.linspace(0, ((batch_size - 1) * self.atoms), batch_size).long().unsqueeze(1).expand(batch_size, self.atoms).type_as(actions) m.view(-1).index_add_(0, (l + offset).view(-1), (pns_a * (u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b) m.view(-1).index_add_(0, (u + offset).view(-1), (pns_a * (b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l) loss = -torch.sum(Variable(m) * ps_a.log(), 1) # Cross-entropy loss (minimises Kullback-Leibler divergence) self.policy_net.zero_grad() (weights * loss).mean().backward() # Importance weight losses nn.utils.clip_grad_norm(self.policy_net.parameters(), self.max_gradient_norm) # Clip gradients (normalising by max value of gradient L2 norm) self.optimiser.step() mem.update_priorities(idxs, loss.data.abs().pow(self.priority_exponent)) # Update priorities of sampled transitions def update_target_net(self): self.target_net.load_state_dict(self.policy_net.state_dict()) def save(self, path): torch.save(self.policy_net.state_dict(), os.path.join(path, 'model.pth')) # Evaluates Q-value based on single state (no batch) def evaluate_q(self, state): return (self.policy_net(state.unsqueeze(0)).data * self.support).sum(2).max(1)[0][0] def train(self): self.policy_net.train() def eval(self): self.policy_net.eval()
class DQNAgent: """ Interacts with and learns from the environment. Vanilla DQN. """ def __init__(self, state_size: int, action_size: int, seed: int): """ Initialize an Agent object. :param state_size: dimension of each state; :param action_size: dimension of each action; :param seed: random seed. """ self.state_size = state_size self.action_size = action_size random.seed(seed) # Q-Network self.network_local = DQN(state_size, action_size, seed).to(DEVICE) self.network_target = DQN(state_size, action_size, seed).to(DEVICE) self.optimizer = optim.Adam(self.network_local.parameters(), lr=LR) # Replay memory self.memory = ReplayBuffer(BUFFER_SIZE, BATCH_SIZE, seed) # Initialize time step (for updating every UPDATE_EVERY steps) self.t_step = 0 def step(self, state, action: int, reward: float, next_state, done): """ Save experiences in the replay memory and check if it's time to learn. :param state: (array_like) current state; :param action: action taken; :param reward: reward received; :param next_state: (array_like) next state; :param done: terminal state indicator; int or bool. """ # Save experience in replay memory self.memory.push(state, action, reward, next_state, done) # Increment time step and compare it to the network update frequency self.t_step = (self.t_step + 1) % UPDATE_EVERY if self.t_step == 0: # Check if there is enough samples in the memory to learn if len(self.memory) > BATCH_SIZE: # sample experiences from memory experiences = self.memory.sample() # learn from sampled experiences self.learn(experiences, GAMMA) def act(self, state, eps: float = 0.): """ Returns actions for given state as per current policy. :param state: (array_like) current state :param eps: epsilon, for epsilon-greedy action selection """ state = torch.from_numpy(state).float().unsqueeze(0).to(DEVICE) self.network_local.eval() with torch.no_grad(): action_values = self.network_local(state) self.network_local.train() # Epsilon-greedy action selection if random.random() > eps: return np.argmax(action_values.cpu().data.numpy()) else: return random.choice(np.arange(self.action_size)) def learn(self, experiences, gamma: float): """ Update value parameters using given batch of experience tuples. :param experiences: (Tuple[torch.Tensor]) tuple of (s, a, r, s', done) tuples; :param gamma: discount factor. """ states, actions, rewards, next_states, dones = experiences # Get max predicted Q values (for next states) from target model Q_targets_next = self.network_target(next_states).detach().max( 1)[0].unsqueeze(1) # Compute Q targets for current states Q_targets = rewards + (gamma * Q_targets_next * (1 - dones)) # Get expected Q values from local model Q_expected = self.network_local(states).gather(1, actions) # Compute loss loss = F.mse_loss(Q_expected, Q_targets) # Minimize the loss self.optimizer.zero_grad() loss.backward() self.optimizer.step() # ------------------- update target network ------------------- # self.soft_update(self.network_local, self.network_target, TAU) @staticmethod def soft_update(local_model, target_model, tau: float): """ Soft update model parameters, θ_target = τ*θ_local + (1 - τ)*θ_target. :param local_model: (PyTorch model) weights will be copied from; :param target_model: (PyTorch model) weights will be copied to; :param tau: interpolation parameter. """ for target_param, local_param in zip(target_model.parameters(), local_model.parameters()): target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data)
class Agent: state: int actions: int history: int = 4 atoms: int = 5 #51 Vmin: float = -10 Vmax: float = 10 lr: float = 1e-5 batch_size: int = 32 discount: float = 0.99 norm_clip: float = 10. def __post_init__(self): self.support = torch.linspace(self.Vmin, self.Vmax, self.atoms) self.delta_z = (self.Vmax - self.Vmin) / (self.atoms - 1) self.online_net = DQN(self.state, self.actions, self.history, self.atoms) self.online_net.train() self.target_net = DQN(self.state, self.actions, self.history, self.atoms) self.update_target_net() self.target_net.train() for param in self.target_net.parameters(): param.requires_grad = False self.optimiser = optim.Adam(self.online_net.parameters(), lr=self.lr) def act(self, state): state = torch.FloatTensor(state).unsqueeze(0) with torch.no_grad(): return (self.online_net(state) * self.support).sum(2).argmax(1).item() def act_e_greedy(self, state, epsilon=0.001): return random.randrange(self.actions) if random.random() < epsilon else self.act(state) def learn(self, buffer): state, action, reward, next_state, terminal, weights, idx = buffer.sample(self.batch_size) state = torch.FloatTensor(state) action = torch.LongTensor(action) reward = torch.FloatTensor(reward) next_state = torch.FloatTensor(next_state) terminal = torch.FloatTensor(terminal) weights = torch.FloatTensor(weights) log_ps = self.online_net(state, log=True) log_ps_a = log_ps[range(self.batch_size), action] with torch.no_grad(): # Calculate nth next state probabilities pns = self.online_net(next_state) dns = self.support.expand_as(pns) * pns argmax_indices_ns = dns.sum(2).argmax(1) self.target_net.sample_noise() pns = self.target_net(next_state) pns_a = pns[range(self.batch_size), argmax_indices_ns] # Compute Bellman operator T applied to z Tz = reward.unsqueeze(1) + (1 - terminal).unsqueeze(1) * self.discount * self.support.unsqueeze(0) # -10 ... 10 + reward Tz.clamp_(min=self.Vmin, max=self.Vmax) # Compute L2 projection of Tz onto fixed support z b = (Tz - self.Vmin) / self.delta_z # 0 ... 4 l, u = b.floor().to(torch.int64), b.ceil().to(torch.int64) # Fix disappearing probability mass when l = b = u (b is int) l[(u > 0) * (l == u)] -= 1 u[(l < (self.atoms - 1)) * (l == u)] += 1 # Distribute probability of Tz m = state.new_zeros(self.batch_size, self.atoms) offset = torch.linspace(0, ((self.batch_size - 1) * self.atoms), self.batch_size).unsqueeze(1).expand(self.batch_size, self.atoms).to(action) m.view(-1).index_add_(0, (l + offset).view(-1), (pns_a * (u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b) m.view(-1).index_add_(0, (u + offset).view(-1), (pns_a * (b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l) loss = -torch.sum(m * log_ps_a, 1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t))) loss = weights * loss # q_values = self.online_net(state) # q_value = q_values[range(self.batch_size), action] # next_q_values = self.target_net(next_state) # next_q_value = next_q_values.max(1)[0] # expected_q_value = reward + self.discount * next_q_value * (1 - terminal) # loss = weights * (q_value - expected_q_value).pow(2) self.optimiser.zero_grad() loss.mean().backward() self.optimiser.step() nn.utils.clip_grad_norm_(self.online_net.parameters(), self.norm_clip) buffer.update_priorities(idx, loss.tolist()) def update_target_net(self): self.target_net.load_state_dict(self.online_net.state_dict()) def sample_noise(self): self.online_net.sample_noise() def save(self, path): torch.save(self.online_net.state_dict(), path) # Evaluates Q-value based on single state (no batch) def evaluate_q(self, state): with torch.no_grad(): return self.online_net(state.unsqueeze(0)).max(1)[0].item() def train(self): self.online_net.train() def eval(self): self.online_net.eval()
while True: state = get_state(env.reset()).to(device) while True: with torch.no_grad(): action = policy_net(state).max(1)[1].view(1, 1) next_state, _, done, _ = env.step(action) if done: break next_state = get_state(next_state).to(device) state = next_state if __name__ == '__main__': # enable cuda is available device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Initialise the game env = gym.make('ChromeDino-v0') # env = gym.make('ChromeDinoNoBrowser-v0') env = make_dino(env, timer=True, frame_stack=True) # Get the number of actions and the dimension of input n_actions = env.action_space.n # initialise networks policy_net = DQN(n_actions=n_actions).to(device) trained_model = torch.load('checkpoints/model_2000.pth') policy_net.load_state_dict(trained_model) policy_net.eval() test(env)
class Agent(object): def __init__(self, args, action_space): self.action_space = action_space self.batch_size = args.batch_size self.discount = args.discount self.online_net = DQN(args, self.action_space).to(device=args.device) self.online_net.train() self.target_net = DQN(args, self.action_space).to(device=args.device) self.update_target_net() self.target_net.train() for param in self.target_net.parameters(): param.requires_grad = False self.optimiser = optim.Adam(self.online_net.parameters(), lr=args.lr, eps=args.adam_eps) self.loss_func = nn.MSELoss() # Acts based on single state (no batch) def act(self, state): with torch.no_grad(): return self.online_net([state]).argmax(1).item() # Acts with an ε-greedy policy (used for evaluation only) def act_e_greedy( self, state, epsilon=0.05): # High ε can reduce evaluation scores drastically return random.randrange( self.action_space) if random.random() < epsilon else self.act( state) def learn(self, mem): # Sample transitions states, actions, next_states, rewards = mem.sample(self.batch_size) q_eval = self.online_net(states).gather( 1, actions.unsqueeze(1)).squeeze() with torch.no_grad(): q_eval_next_a = self.online_net(next_states).argmax(1) q_next = self.target_net(next_states) q_target = rewards + self.discount * q_next.gather( 1, q_eval_next_a.unsqueeze(1)).squeeze() loss = self.loss_func(q_eval, q_target) self.online_net.zero_grad() loss.backward() self.optimiser.step() def update_target_net(self): self.target_net.load_state_dict(self.online_net.state_dict()) # Save model parameters on current device (don't move model between devices) def save(self, path): torch.save(self.online_net.state_dict(), path + '.pth') # Evaluates Q-value based on single state (no batch) def evaluate_q(self, state): with torch.no_grad(): return (self.online_net([state])).max(1)[0].item() def train(self): self.online_net.train() def eval(self): self.online_net.eval()
class Agent(): def __init__(self, args, env): self.action_space = env.action_space() self.atoms = args.atoms self.Vmin = args.V_min self.Vmax = args.V_max self.support = torch.linspace(args.V_min, args.V_max, self.atoms).to( device=args.device) # Support (range) of z self.delta_z = (args.V_max - args.V_min) / (self.atoms - 1) self.batch_size = args.batch_size self.n = args.multi_step self.discount = args.discount self.norm_clip = args.norm_clip self.online_net = DQN(args, self.action_space).to(device=args.device) if args.model: # Load pretrained model if provided if os.path.isfile(args.model): state_dict = torch.load( args.model, map_location='cpu' ) # Always load tensors onto CPU by default, will shift to GPU if necessary if 'conv1.weight' in state_dict.keys(): for old_key, new_key in (('conv1.weight', 'convs.0.weight'), ('conv1.bias', 'convs.0.bias'), ('conv2.weight', 'convs.2.weight'), ('conv2.bias', 'convs.2.bias'), ('conv3.weight', 'convs.4.weight'), ('conv3.bias', 'convs.4.bias')): state_dict[new_key] = state_dict[ old_key] # Re-map state dict for old pretrained models del state_dict[ old_key] # Delete old keys for strict load_state_dict self.online_net.load_state_dict(state_dict) print("Loading pretrained model: " + args.model) else: # Raise error if incorrect model path provided raise FileNotFoundError(args.model) self.online_net.train() self.target_net = DQN(args, self.action_space).to(device=args.device) self.update_target_net() self.target_net.train() for param in self.target_net.parameters(): param.requires_grad = False # self.optimiser = optim.Adam(self.online_net.parameters(), lr=args.learning_rate, eps=args.adam_eps) self.convs_optimiser = optim.Adam(self.online_net.convs.parameters(), lr=args.learning_rate, eps=args.adam_eps) self.linear_optimiser = optim.Adam(chain( self.online_net.fc_h_v.parameters(), self.online_net.fc_h_a.parameters(), self.online_net.fc_z_v.parameters(), self.online_net.fc_z_a.parameters()), lr=args.learning_rate, eps=args.adam_eps) # Resets noisy weights in all linear layers (of online net only) def reset_noise(self): self.online_net.reset_noise() # Acts based on single state (no batch) def act(self, state): with torch.no_grad(): # don't count these calls since it is accounted for after "action = dqn.act(state)" in main.py ret = (self.online_net(state.unsqueeze(0)) * self.support).sum(2).argmax(1).item() return ret # Acts with an ε-greedy policy (used for evaluation only) def act_e_greedy( self, state, epsilon=0.001): # High ε can reduce evaluation scores drastically return np.random.randint( 0, self.action_space ) if np.random.random() < epsilon else self.act(state) def learn(self, mem, freeze=False): # Sample transitions idxs, states, actions, returns, next_states, nonterminals, weights, _ = mem.sample( self.batch_size) # Calculate current state probabilities (online network noise already sampled) log_ps = self.online_net( states, log=True) # Log probabilities log p(s_t, ·; θonline) log_ps_a = log_ps[range(self.batch_size), actions] # log p(s_t, a_t; θonline) with torch.no_grad(): # Calculate nth next state probabilities pns = self.online_net( next_states) # Probabilities p(s_t+n, ·; θonline) dns = self.support.expand_as( pns) * pns # Distribution d_t+n = (z, p(s_t+n, ·; θonline)) argmax_indices_ns = dns.sum(2).argmax( 1 ) # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))] self.target_net.reset_noise() # Sample new target net noise pns = self.target_net( next_states) # Probabilities p(s_t+n, ·; θtarget) pns_a = pns[range( self.batch_size ), argmax_indices_ns] # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget) # Compute Tz (Bellman operator T applied to z) Tz = returns.unsqueeze(1) + nonterminals * ( self.discount**self.n) * self.support.unsqueeze( 0) # Tz = R^n + (γ^n)z (accounting for terminal states) Tz = Tz.clamp(min=self.Vmin, max=self.Vmax) # Clamp between supported values # Compute L2 projection of Tz onto fixed support z b = (Tz - self.Vmin) / self.delta_z # b = (Tz - Vmin) / Δz l, u = b.floor().to(torch.int64), b.ceil().to(torch.int64) # Fix disappearing probability mass when l = b = u (b is int) l[(u > 0) * (l == u)] -= 1 u[(l < (self.atoms - 1)) * (l == u)] += 1 # Distribute probability of Tz m = states.new_zeros(self.batch_size, self.atoms) offset = torch.linspace(0, ((self.batch_size - 1) * self.atoms), self.batch_size).unsqueeze(1).expand( self.batch_size, self.atoms).to(actions) m.view(-1).index_add_( 0, (l + offset).view(-1), (pns_a * (u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b) m.view(-1).index_add_( 0, (u + offset).view(-1), (pns_a * (b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l) loss = -torch.sum( m * log_ps_a, 1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t))) self.online_net.zero_grad() loss.mean().backward( ) # Backpropagate importance-weighted minibatch loss clip_grad_norm_(self.online_net.parameters(), self.norm_clip) # Clip gradients by L2 norm # self.optimiser.step() if not freeze: self.convs_optimiser.step() self.linear_optimiser.step() def learn_with_latent(self, latent_mem): # Sample transitions idxs, states, actions, returns, next_states, nonterminals, weights, ns = latent_mem.sample( self.batch_size) # Calculate current state probabilities (online network noise already sampled) log_ps = self.online_net.forward_with_latent( states, log=True) # Log probabilities log p(s_t, ·; θonline) log_ps_a = log_ps[range(self.batch_size), actions] # log p(s_t, a_t; θonline) with torch.no_grad(): # Calculate nth next state probabilities pns = self.online_net.forward_with_latent( next_states) # Probabilities p(s_t+n, ·; θonline) dns = self.support.expand_as( pns) * pns # Distribution ds_t+n = (z, p(s_t+n, ·; θonline)) argmax_indices_ns = dns.sum(2).argmax( 1 ) # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))] self.target_net.reset_noise() # Sample new target net noise pns = self.target_net.forward_with_latent( next_states) # Probabilities p(s_t+n, ·; θtarget) pns_a = pns[range( self.batch_size ), argmax_indices_ns] # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget) # use ns instead of self.n since n is possibly different for each sequence in the batch ns = torch.tensor(ns, device=latent_mem.device).unsqueeze(1) # Compute Tz (Bellman operator T applied to z) Tz = returns.unsqueeze(1) + nonterminals * ( self.discount**ns) * self.support.unsqueeze( 0) # Tz = R^n + (γ^n)z (accounting for terminal states) Tz = Tz.clamp(min=self.Vmin, max=self.Vmax) # Clamp between supported values # Compute L2 projection of Tz onto fixed support z b = (Tz - self.Vmin) / self.delta_z # b = (Tz - Vmin) / Δz l, u = b.floor().to(torch.int64), b.ceil().to(torch.int64) # Fix disappearing probability mass when l = b = u (b is int) l[(u > 0) * (l == u)] -= 1 u[(l < (self.atoms - 1)) * (l == u)] += 1 # Distribute probability of Tz m = states.new_zeros(self.batch_size, self.atoms) offset = torch.linspace(0, ((self.batch_size - 1) * self.atoms), self.batch_size).unsqueeze(1).expand( self.batch_size, self.atoms).to(actions) m.view(-1).index_add_( 0, (l + offset).view(-1), (pns_a * (u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b) m.view(-1).index_add_( 0, (u + offset).view(-1), (pns_a * (b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l) loss = -torch.sum( m * log_ps_a, 1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t))) self.online_net.zero_grad() loss.mean().backward( ) # Backpropagate importance-weighted minibatch loss clip_grad_norm_(self.online_net.parameters(), self.norm_clip) # Clip gradients by L2 norm # self.optimiser.step() self.linear_optimiser.step() def update_target_net(self): self.target_net.load_state_dict(self.online_net.state_dict()) # Save model parameters on current device (don't move model between devices) def save(self, path, name='model.pth'): torch.save(self.online_net.state_dict(), os.path.join(path, name)) # Evaluates Q-value based on single state (no batch) def evaluate_q(self, state): with torch.no_grad(): return (self.online_net(state.unsqueeze(0)) * self.support).sum(2).max(1)[0].item() def train(self): self.online_net.train() def eval(self): self.online_net.eval()
class DQNAgent: """ 初始化 @:param env_id : gym环境id """ def __init__(self, env_id, config): # gym self._env_id = env_id self._env = gym.make(env_id) self._state_size = self._env.observation_space.shape[0] self._action_size = self._env.action_space.n # 参数 self._gamma = config.gamma self._learning_rate = config.lr self._reward_boundary = config.reward_boundary self._device = torch.device("cuda" if config.cuda and torch.cuda.is_available() else "cpu") # model self._model = DQN(self._state_size, self._action_size).to(self._device) self._optimizer = torch.optim.Adam(self._model.parameters(), lr=self._learning_rate) # 经验池 self._replay_buffer = deque(maxlen=config.buffer_size) self._mini_batch = config.mini_batch # epsilon self._epsilon = config.epsilon self._epsilon_min = config.epsilon_min self._epsilon_decay = config.epsilon_decay """ 将observation放入双向队列中,队列满时自动删除最旧的元素 """ def remember(self, state, action, next_state, reward, done): self._replay_buffer.append((state, action, next_state, reward, done)) # epsilon幂指数下降 if len(self._replay_buffer) > self._mini_batch: if self._epsilon > self._epsilon_min: self._epsilon *= self._epsilon_decay pass """ epsilon-greedy action """ def act(self, state): # 类似模拟退火,random返回[0,1] if np.random.random() <= self._epsilon: return random.randrange(self._action_size) else: # numpy转成tensor,unsqueeze在下标0处新增一个维度 state = torch.tensor(state, dtype=torch.float).unsqueeze(0).to(self._device) # 模型预测 predict = self._model(state) # max在第1维处取最大,[1]为下标,[0]为值, [512*2]-> [521] return predict.max(1)[1].item() pass """ 训练 1、从双向队列中采样mini_batch 2、预测next_state 3、更新优化器 """ def replay(self): if len(self._replay_buffer) < self._mini_batch: return # 1、从双向队列中采样mini_batch mini_batch = random.sample(self._replay_buffer, self._mini_batch) # 载入方式一 # state = np.zeros((self._mini_batch, self._state_size)) # next_state = np.zeros((self._mini_batch, self._state_size)) # action, reward, done = [], [], [] # # for i in range(self._mini_batch): # state[i] = mini_batch[i][0] # action.append(mini_batch[i][1]) # next_state[i] = mini_batch[i][2] # reward.append(mini_batch[i][3]) # done.append(mini_batch[i][4]) # 载入方式二 state, action, next_state, reward, done = zip(*mini_batch) state = torch.tensor(state, dtype=torch.float).to(self._device) action = torch.tensor(action, dtype=torch.long).to(self._device) next_state = torch.tensor(next_state, dtype=torch.float).to(self._device) reward = torch.tensor(reward, dtype=torch.float).to(self._device) done = torch.tensor(done, dtype=torch.float).to(self._device) # 2、预测next_state q_target = reward + \ self._gamma * self._model(next_state).to(self._device).max(1)[0] * (1 - done) q_values = self._model(state).to(self._device).gather(1, action.unsqueeze(1)).squeeze(1) loss_func = nn.MSELoss() loss = loss_func(q_values, q_target) # loss = (q_values - q_target.detach()).pow(2).mean() # 3、更新优化器 self._optimizer.zero_grad() loss.backward() self._optimizer.step() return loss.item() """ 1、渲染gym环境开始交互 2、训练模型 """ def training(self): writer = SummaryWriter(comment="-train-" + self._env_id) print(self._model) # 参数 frame_index = 0 episode_index = 1 best_mean_reward = None mean_reward = 0 total_rewards = [] while mean_reward < self._reward_boundary: state = self._env.reset() # 一轮结束,reward置零 episode_reward = 0 while True: # 1、渲染gym环境开始交互 self._env.render() # 选择action进行交互 action = self.act(state) next_state, reward, done, _ = self._env.step(action) self.remember(state, action, next_state, reward, done) state = next_state frame_index += 1 episode_reward += reward # 2、训练模型 loss = self.replay() # 游戏结束,开始训练模型 if done: if loss is not None: print("episode: %4d, frames: %5d, reward: %5f, loss: %4f, epsilon: %4f" % ( episode_index, frame_index, np.mean(total_rewards[-10:]), loss, self._epsilon)) episode_index += 1 total_rewards.append(episode_reward) mean_reward = np.mean(total_rewards[-10:]) writer.add_scalar("epsilon", self._epsilon, frame_index) writer.add_scalar("episode_reward", episode_reward, frame_index) writer.add_scalar("mean_reward", mean_reward, frame_index) if best_mean_reward is None or best_mean_reward < mean_reward: torch.save(self._model.state_dict(), "training-best.dat") break self._env.close() pass def test(self, model_path): if model_path is None: return self._model.load_state_dict(torch.load(model_path)) self._model.eval() total_rewards = [] for episode_index in range(10): episode_reward = 0 done = False state = self._env.reset() while not done: action = self.act(state) next_state, reward, done, _ = self._env.step(action) state = next_state episode_reward += reward total_rewards.append(episode_reward) print("episode: %4d, reward: %5f" % (episode_index, np.mean(total_rewards[-10:])))
class Agent: """ The intelligent agent of the simulation. Set the model of the neural network used and general parameters. It is responsible to select the actions, optimize the neural network and manage the models. """ def __init__(self, action_set, train=True, load_path=None): #1. Initialize agent params self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.action_set = action_set self.action_number = len(action_set) self.steps_done = 0 self.epsilon = Config.EPS_START self.episode_durations = [] #2. Build networks self.policy_net = DQN().to(self.device) self.target_net = DQN().to(self.device) self.optimizer = optim.RMSprop(self.policy_net.parameters(), lr=Config.LEARNING_RATE) if not train: self.optimizer = optim.RMSprop(self.policy_net.parameters(), lr=0) self.policy_net.load(load_path, optimizer=self.optimizer) self.policy_net.eval() self.target_net.load_state_dict(self.policy_net.state_dict()) self.target_net.eval() #3. Create Prioritized Experience Replay Memory self.memory = Memory(Config.MEMORY_SIZE) def append_sample(self, state, action, next_state, reward): """ save sample (error,<s,a,s',r>) to the replay memory """ # Define if is the end of the simulation done = True if next_state is None else False # Compute Q(s_t, a) - the model computes Q(s_t), then we select the columns of actions taken state_action_values = self.policy_net(state) state_action_values = state_action_values.gather(1, action.view(-1,1)) if not done: # Compute argmax Q(s', a; θ) next_state_actions = self.policy_net(next_state).max(1)[1].detach().unsqueeze(1) # Compute Q(s', argmax Q(s', a; θ), θ-) next_state_values = self.target_net(next_state).gather(1, next_state_actions).squeeze(1).detach() # Compute the expected Q values expected_state_action_values = (next_state_values * Config.GAMMA) + reward else: expected_state_action_values = reward error = abs(state_action_values - expected_state_action_values).data.cpu().numpy() self.memory.add(error, state, action, next_state, reward) def select_action(self, state, train=True): """ Selet the best action according to the Q-values outputed from the neural network Parameters ---------- state: float ndarray The current state on the simulation train: bool Define if we are evaluating or trainning the model Returns ------- a.max(1)[1]: int The action with the highest Q-value a.max(0): float The Q-value of the action taken """ global steps_done sample = random.random() #1. Perform a epsilon-greedy algorithm #a. set the value for epsilon self.epsilon = Config.EPS_END + (Config.EPS_START - Config.EPS_END) * \ math.exp(-1. * self.steps_done / Config.EPS_DECAY) self.steps_done += 1 #b. make the decision for selecting a random action or selecting an action from the neural network if sample > self.epsilon or (not train): # select an action from the neural network with torch.no_grad(): # a <- argmax Q(s, theta) a = self.policy_net(state) return a.max(1)[1].view(1, 1), a.max(0) else: # select a random action print('random action') return torch.tensor([[random.randrange(2)]], device=self.device, dtype=torch.long), None """ def select_action(self, state, train=True): Selet the best action according to the Q-values outputed from the neural network Parameters ---------- state: float ndarray The current state on the simulation train: bool Define if we are evaluating or trainning the model Returns ------- a.max(1)[1]: int The action with the highest Q-value a.max(0): float The Q-value of the action taken global steps_done sample = random.random() #1. Perform a epsilon-greedy algorithm #a. set the value for epsilon self.epsilon = Config.EPS_END + (Config.EPS_START - Config.EPS_END) * \ math.exp(-1. * self.steps_done / Config.EPS_DECAY) self.steps_done += 1 #b. make the decision for selecting a random action or selecting an action from the neural network if sample > self.epsilon or (not train): # select an action from the neural network with torch.no_grad(): # a <- argmax Q(s, theta) #set the network to train mode is important to enable dropout self.policy_net.train() output_list = [] # Retrieve the outputs from neural network feedfoward n times to build a statistic model for i in range(Config.STOCHASTIC_PASSES): #print(agent.policy_net(data)) output_list.append(torch.unsqueeze(F.softmax(self.policy_net(state)), 0)) #print(output_list[i]) self.policy_net.eval() # The result of the network is the mean of n passes output_mean = torch.cat(output_list, 0).mean(0) q_value = output_mean.data.cpu().numpy().max() action = output_mean.max(1)[1].view(1, 1) uncertainty = torch.cat(output_list, 0).var(0).mean().item() return action, q_value, uncertainty else: # select a random action print('random action') return torch.tensor([[random.randrange(2)]], device=self.device, dtype=torch.long), None, None """ def optimize_model(self): """ Perform one step of optimization on the neural network """ if self.memory.tree.n_entries < Config.BATCH_SIZE: return transitions, idxs, is_weights = self.memory.sample(Config.BATCH_SIZE) # Transpose the batch (see http://stackoverflow.com/a/19343/3343043 for detailed explanation). batch = Transition(*zip(*transitions)) # Compute a mask of non-final states and concatenate the batch elements non_final_mask = torch.tensor(tuple(map(lambda s: s is not None, batch.next_state)), device=self.device, dtype=torch.uint8) non_final_next_states = torch.cat([s for s in batch.next_state if s is not None]) state_batch = torch.cat(batch.state) action_batch = torch.cat(batch.action) reward_batch = torch.cat(batch.reward) # Compute Q(s_t, a) - the model computes Q(s_t), then we select the columns of actions taken state_action_values = self.policy_net(state_batch).gather(1, action_batch) # Compute argmax Q(s', a; θ) next_state_actions = self.policy_net(non_final_next_states).max(1)[1].detach().unsqueeze(1) # Compute Q(s', argmax Q(s', a; θ), θ-) next_state_values = torch.zeros(Config.BATCH_SIZE, device=self.device) next_state_values[non_final_mask] = self.target_net(non_final_next_states).gather(1, next_state_actions).squeeze(1).detach() # Compute the expected Q values expected_state_action_values = (next_state_values * Config.GAMMA) + reward_batch # Update priorities errors = torch.abs(state_action_values.squeeze() - expected_state_action_values).data.cpu().numpy() # update priority for i in range(Config.BATCH_SIZE): idx = idxs[i] self.memory.update(idx, errors[i]) # Compute Huber loss loss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1)) loss_return = loss.item() # Optimize the model self.optimizer.zero_grad() loss.backward() for param in self.policy_net.parameters(): param.grad.data.clamp_(-1, 1) self.optimizer.step() return loss_return def save(self, step, logs_path, label): """ Save the model on hard disc Parameters ---------- step: int current step on the simulation logs_path: string path to where we will store the model label: string label that will be used to store the model """ os.makedirs(logs_path + label, exist_ok=True) full_label = label + str(step) + '.pth' logs_path = os.path.join(logs_path, label, full_label) self.policy_net.save(logs_path, step=step, optimizer=self.optimizer) def restore(self, logs_path): """ Load the model from hard disc Parameters ---------- logs_path: string path to where we will store the model """ self.policy_net.load(logs_path) self.target_net.load(logs_path)
class Agent(): def __init__(self, args, env): self.action_space = env.action_space() self.atoms = args.atoms self.Vmin = args.V_min self.Vmax = args.V_max self.support = torch.linspace(args.V_min, args.V_max, args.atoms) # Support (range) of z self.delta_z = (args.V_max - args.V_min) / (args.atoms - 1) self.batch_size = args.batch_size self.n = args.multi_step self.discount = args.discount self.online_net = DQN(args, self.action_space) if args.model and os.path.isfile(args.model): self.online_net.load_state_dict( torch.load(args.model, map_location='cpu')) self.online_net.train() self.target_net = DQN(args, self.action_space) self.update_target_net() self.target_net.train() for param in self.target_net.parameters(): param.requires_grad = False self.optimiser = optim.Adam(self.online_net.parameters(), lr=args.lr, eps=args.adam_eps) if args.cuda: self.online_net.cuda() self.target_net.cuda() self.support = self.support.cuda() # Resets noisy weights in all linear layers (of online net only) def reset_noise(self): self.online_net.reset_noise() # Acts based on single state (no batch) def act(self, state): return (self.online_net(state.unsqueeze(0)).data * self.support).sum(2).max(1)[1][0] # Acts with an ε-greedy policy def act_e_greedy(self, state, epsilon=0.001): return random.randrange( self.action_space) if random.random() < epsilon else self.act( state) def learn(self, mem): # Sample transitions idxs, states, actions, returns, next_states, nonterminals, weights = mem.sample( self.batch_size) # Calculate current state probabilities self.online_net.reset_noise() # Sample new noise for online network ps = self.online_net(states) # Probabilities p(s_t, ·; θonline) ps_a = ps[range(self.batch_size), actions] # p(s_t, a_t; θonline) # Calculate nth next state probabilities self.online_net.reset_noise() # Sample new noise for action selection pns = self.online_net( next_states).data # Probabilities p(s_t+n, ·; θonline) dns = self.support.expand_as( pns) * pns # Distribution d_t+n = (z, p(s_t+n, ·; θonline)) argmax_indices_ns = dns.sum(2).max( 1 )[1] # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))] self.target_net.reset_noise() # Sample new target net noise pns = self.target_net( next_states).data # Probabilities p(s_t+n, ·; θtarget) pns_a = pns[range( self.batch_size ), argmax_indices_ns] # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget) # Compute Tz (Bellman operator T applied to z) Tz = returns.unsqueeze(1) + nonterminals * ( self.discount**self.n) * self.support.unsqueeze( 0) # Tz = R^n + (γ^n)z (accounting for terminal states) Tz = Tz.clamp(min=self.Vmin, max=self.Vmax) # Clamp between supported values # Compute L2 projection of Tz onto fixed support z b = (Tz - self.Vmin) / self.delta_z # b = (Tz - Vmin) / Δz l, u = b.floor().long(), b.ceil().long() # Fix disappearing probability mass when l = b = u (b is int) l[(u > 0) * (l == u)] -= 1 u[(l < (self.atoms - 1)) * (l == u)] += 1 # Distribute probability of Tz m = states.data.new(self.batch_size, self.atoms).zero_() offset = torch.linspace(0, ((self.batch_size - 1) * self.atoms), self.batch_size).unsqueeze(1).expand( self.batch_size, self.atoms).type_as(actions) m.view(-1).index_add_( 0, (l + offset).view(-1), (pns_a * (u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b) m.view(-1).index_add_( 0, (u + offset).view(-1), (pns_a * (b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l) ps_a = ps_a.clamp(min=1e-3) # Clamp for numerical stability in log loss = -torch.sum( Variable(m) * ps_a.log(), 1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t))) self.online_net.zero_grad() (weights * loss).mean().backward() # Importance weight losses self.optimiser.step() mem.update_priorities( idxs, loss.data) # Update priorities of sampled transitions def update_target_net(self): self.target_net.load_state_dict(self.online_net.state_dict()) def save(self, path): torch.save(self.online_net.state_dict(), os.path.join(path, 'model.pth')) # Evaluates Q-value based on single state (no batch) def evaluate_q(self, state): return (self.online_net(state.unsqueeze(0)).data * self.support).sum(2).max(1)[0][0] def train(self): self.online_net.train() def eval(self): self.online_net.eval()
class Agent(object): """ all improvments from Rainbow research work """ def __init__(self, args, state_size, action_size): """ Args: param1 (args): args param2 (int): args param3 (int): args """ self.action_size = action_size self.state_size = state_size self.atoms = args.atoms self.V_min = args.V_min self.V_max = args.V_max self.device = args.device self.support = torch.linspace(args.V_min, args.V_max, self.atoms).to( device=self.device) # Support (range) of z self.delta_z = (args.V_max - args.V_min) / (self.atoms - 1) self.batch_size = args.batch_size self.n = args.multi_step self.discount = args.discount self.qnetwork_local = DQN(args, self.state_size, self.action_size).to(device=args.device) if args.model and os.path.isfile(args.model): # Always load tensors onto CPU by default, will shift to GPU if necessary self.qnetwork_local.load_state_dict( torch.load(args.model, map_location='cpu')) self.qnetwork_local.train() self.target_net = DQN(args, self.state_size, self.action_size).to(device=args.device) self.update_target_net() self.target_net.train() for param in self.target_net.parameters(): param.requires_grad = False self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=args.lr, eps=args.adam_eps) def reset_noise(self): """ resets noisy weights in all linear layers """ self.qnetwork_local.reset_noise() def act(self, state): """ acts greedy(max) based on a single state Args: param1 (int) : state """ with torch.no_grad(): return (self.qnetwork_local(state.unsqueeze(0).to(self.device)) * self.support).sum(2).argmax(1).item() def act_e_greedy(self, state, epsilon=0.001): """ acts with epsilon greedy policy epsilon exploration vs exploitation traide off Args: param1(int): state param2(float): epsilon Return : action int number between 0 and 4 """ return np.random.randint( 0, self.action_size) if np.random.random() < epsilon else self.act( state) def learn(self, mem): """ uses samples with the given batch size to improve the Q function Args: param1 (Experince Replay Buffer) : mem """ # Sample transitions idxs, states, actions, returns, next_states, nonterminals, weights = mem.sample( self.batch_size) # Calculate current state probabilities (online network noise already sampled) log_ps = self.qnetwork_local( states, log=True) # Log probabilities log p(s_t, *; theta online) log_ps_a = log_ps[range(self.batch_size), actions] # log p(s_t, a_t; theat online) with torch.no_grad(): # Calculate nth next state probabilities pns = self.qnetwork_local( next_states) # Probabilities p(s_t+n, *; theta online) dns = self.support.expand_as( pns ) * pns # Distribution d_t+n = (z, p(s_t+n, *; theat online)) argmax_indices_ns = dns.sum(2).argmax( 1 ) # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; theat online))] self.target_net.reset_noise() # Sample new target net noise pns = self.target_net( next_states) # Probabilities p(s_t+n, ; theata target) pns_a = pns[range( self.batch_size ), argmax_indices_ns] # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; theat online))]; theat target) # Compute Tz (Bellman operator T applied to z) Tz = returns.unsqueeze(1) + nonterminals * ( self.discount**self.n ) * self.support.unsqueeze( 0) # Tz = R^n + (discoit ^n)z (accounting for terminal states) Tz = Tz.clamp(min=self.V_min, max=self.V_max) # Clamp between supported values # Compute L2 projection of Tz onto fixed support z b = (Tz - self.V_min) / self.delta_z # b = (Tz - Vmin) / delta z l, u = b.floor().to(torch.int64), b.ceil().to(torch.int64) # Fix disappearing probability mass when l = b = u (b is int) l[(u > 0) * (l == u)] -= 1 u[(l < (self.atoms - 1)) * (l == u)] += 1 # Distribute probability of Tz m = states.new_zeros(self.batch_size, self.atoms) offset = torch.linspace(0, ((self.batch_size - 1) * self.atoms), self.batch_size).unsqueeze(1).expand( self.batch_size, self.atoms).to(actions) m.view(-1).index_add_( 0, (l + offset).view(-1), (pns_a * (u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b) m.view(-1).index_add_( 0, (u + offset).view(-1), (pns_a * (b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l) loss = -torch.sum( m * log_ps_a, 1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t))) self.qnetwork_local.zero_grad() (weights * loss).mean().backward( ) # Backpropagate importance-weighted minibatch loss self.optimizer.step() mem.update_priorities(idxs, loss.detach().cpu().numpy() ) # Update priorities of sampled transitions self.soft_update() def soft_update(self, tau=1e-3): """ swaps the network weights from the online to the target Args: param1 (float): tau """ for target_param, local_param in zip(self.target_net.parameters(), self.qnetwork_local.parameters()): target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data) def update_target_net(self): """ copy the model weights from the online to the target network """ self.target_net.load_state_dict(self.qnetwork_local.state_dict()) def save(self, path): """ save the model weights to a file Args: param1 (string): pathname """ torch.save(self.qnetwork_local.state_dict(), os.path.join(path, 'model.pth')) def evaluate_q(self, state): """ Evaluates Q-value based on single state """ with torch.no_grad(): return (self.qnetwork_local(state.unsqueeze(0)) * self.support).sum(2).max(1)[0].item() def train(self): """ activates the backprob. layers for the online network """ self.qnetwork_local.train() def eval(self): """ invoke the eval from the online network deactivates the backprob layers like dropout will work in eval model instead """ self.qnetwork_local.eval()