def test(L, mouse_initial_indices, rewardlist, actions_list): online_net = QNet(3, 4).to(device) online_net.load_state_dict( torch.load("./qlearning_model", map_location=device)) env = deepcopy(L) done = False eaubue = 0. steps = 0 score = 0 if mouse_initial_indices is None: all_possible_starting_positions = np.array([*np.where(L == 1)]).T mouse_initial_indices = all_possible_starting_positions[ np.random.choice(range(len(all_possible_starting_positions)))] state = np.array(mouse_initial_indices) state = torch.Tensor(state).to(device) state = state.unsqueeze(0) def progress_loop(done, steps, state, score, eaubue): steps += 1 action = get_action(state, online_net, 1, env, True, eaubue) displacement = np.array(actions_list[action]) newstate = state + torch.Tensor(displacement).to(device) if env[int(newstate[0][0].tolist()), int(newstate[0][1].tolist())] != 0: next_state = newstate displayer.main_canva.move(displayer.mouse, *(displacement * displayer.square_size)) reward = rewardlist[env[int(newstate[0][0].tolist()), int(newstate[0][1].tolist())]] if env[int(newstate[0][0].tolist()), int(newstate[0][1].tolist())] == 2: done = True if env[int(newstate[0][0].tolist()), int(newstate[0][1].tolist() )] == 4: #if the mouse is in the water env[int(newstate[0][0].tolist()), int(newstate[0][1].tolist())] = 5 #there is no more water eaubue = 1. else: next_state = state reward = rewardlist[0] score += reward state = next_state print('position : ', state.tolist()[0], score) if done is False: displayer.window.after( 800, lambda: progress_loop(done, steps, state, score, eaubue)) displayer = Displayer() displayer.create_labyrinth(L, mouse_initial_indices) progress_loop(done, steps, state, score, 0.) displayer.window.mainloop()
def main(): if not (os.path.isdir("logs")): os.makedirs("logs") working_dir = "logs/" + args.dir if not (os.path.isdir(working_dir)): raise NameError(args.dir + " does not exist in dir logs") print(args) env = QubeSwingupEnv(use_simulator=args.sim, batch_size= 2048*4) num_inputs = env.observation_space.shape[0] num_actions = NUMBER_OF_ACTIONS print('state size:', num_inputs) print('action size:', num_actions) net = QNet(num_inputs, num_actions) if not args.new_net else QNet_more_layers(num_inputs, num_actions) net.load_state_dict(torch.load(working_dir + "/best_model.pth", map_location=torch.device(device))) net.to(device) net.eval() running_score = 0 epsilon = 1.0 steps = 0 beta = beta_start loss = 0 best_running_score = -1000 for e in range(1): done = False score = 0 state = env.reset() state = torch.Tensor(state).to(device) state = state.unsqueeze(0) while not done: steps += 1 action = get_continuous_action(get_action(state, net)) if np.abs(state[0][1].item()) < deg2rad(25): action = pd_control_policy(state.cpu().numpy()[0])[0] next_state, reward, done, info = env.step(action) reward = give_me_reward(info["alpha"], info["theta"]) if args.sim: env.render() reward = give_me_reward(info["alpha"], info["theta"]) if done: print(info) print("theta:" , info["theta"] * 180/np.pi) next_state = torch.Tensor(next_state).to(device) next_state = next_state.unsqueeze(0) score += reward state = next_state running_score = 0.99 * running_score + 0.01 * score print('{} episode | running_score: {:.2f} | score: {:.2f} | steps: {} '.format(e, running_score, score, steps)) env.close()
def main(): env = gym.make(args.env_name) env.seed(500) torch.manual_seed(500) img_shape = env.observation_space.shape num_actions = 3 print('image size:', img_shape) print('action size:', num_actions) net = QNet(num_actions) net.load_state_dict(torch.load(args.save_path + 'model.pth')) net.to(device) net.eval() epsilon = 0 for e in range(5): done = False score = 0 state = env.reset() state = pre_process(state) state = torch.Tensor(state).to(device) history = torch.stack((state, state, state, state)) for i in range(3): action = env.action_space.sample() state, reward, done, info = env.step(action) state = pre_process(state) state = torch.Tensor(state).to(device) state = state.unsqueeze(0) history = torch.cat((state, history[:-1]), dim=0) while not done: if args.render: env.render() steps += 1 qvalue = net(history.unsqueeze(0)) action = get_action(0, qvalue, num_actions) next_state, reward, done, info = env.step(action + 1) next_state = pre_process(next_state) next_state = torch.Tensor(next_state).to(device) next_state = next_state.unsqueeze(0) next_history = torch.cat((next_state, history[:-1]), dim=0) score += reward history = next_history print('{} episode | score: {:.2f}'.format(e, score))
def main(): env = gym.make(env_name) env.seed(500) torch.manual_seed(500) num_inputs = env.observation_space.shape[0] num_actions = env.action_space.n print('state size:', num_inputs) print('action size:', num_actions) online_net = QNet(num_inputs, num_actions) target_net = QNet(num_inputs, num_actions) target_net.load_state_dict(online_net.state_dict()) online_net.share_memory() target_net.share_memory() optimizer = SharedAdam(online_net.parameters(), lr=lr) global_ep, global_ep_r, res_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue() writer = SummaryWriter('logs') online_net.to(device) target_net.to(device) online_net.train() target_net.train() workers = [ Worker(online_net, target_net, optimizer, global_ep, global_ep_r, res_queue, i) for i in range(mp.cpu_count()) ] [w.start() for w in workers] res = [] while True: r = res_queue.get() if r is not None: res.append(r) [ep, ep_r, loss] = r writer.add_scalar('log/score', float(ep_r), ep) writer.add_scalar('log/loss', float(loss), ep) else: break [w.join() for w in workers]
def main(): env = gym.make(args.env_name) env.seed(500) torch.manual_seed(500) num_inputs = env.observation_space.shape[0] num_actions = env.action_space.n print('state size:', num_inputs) print('action size:', num_actions) net = QNet(num_inputs, num_actions) net.load_state_dict(torch.load(args.save_path + 'model.pth')) net.to(device) net.eval() running_score = 0 steps = 0 for e in range(5): done = False score = 0 state = env.reset() state = torch.Tensor(state).to(device) state = state.unsqueeze(0) while not done: env.render() steps += 1 qvalue = net(state) action = get_action(qvalue) next_state, reward, done, _ = env.step(action) next_state = torch.Tensor(next_state).to(device) next_state = next_state.unsqueeze(0) score += reward state = next_state print('{} episode | score: {:.2f}'.format(e, score))
def main(): net = QNet().cuda().train() # print(net) optimizer = optim.SGD([{ 'params': [ param for name, param in net.named_parameters() if name[-4:] == 'bias' ], 'lr': 2 * args['lr'] }, { 'params': [ param for name, param in net.named_parameters() if name[-4:] != 'bias' ], 'lr': args['lr'], 'weight_decay': args['weight_decay'] }], momentum=args['momentum']) if len(args['snapshot']) > 0: print('training resumes from ' + args['snapshot']) net.load_state_dict( torch.load( os.path.join(ckpt_path, exp_name, args['snapshot'] + '.pth'))) optimizer.load_state_dict( torch.load( os.path.join(ckpt_path, exp_name, args['snapshot'] + '_optim.pth'))) optimizer.param_groups[0]['lr'] = 2 * args['lr'] optimizer.param_groups[1]['lr'] = args['lr'] check_mkdir(ckpt_path) check_mkdir(os.path.join(ckpt_path, exp_name)) open(log_path, 'w').write(str(args) + '\n\n') train(net, optimizer)
def __init__(self, run): self.run = run ckpt_dir = os.path.join(run, 'ckpt') ckpts = glob2.glob(os.path.join(ckpt_dir, '*.pth')) assert ckpts, "No checkpoints to resume from!" def get_epoch(ckpt_url): s = re.findall("ckpt_e(\d+).pth", ckpt_url) epoch = int(s[0]) if s else -1 return epoch, ckpt_url start_epoch, ckpt = max(get_epoch(c) for c in ckpts) print('Checkpoint:', ckpt) if torch.cuda.is_available(): model = QNet().cuda() else: model = QNet() ckpt = torch.load(ckpt) model.load_state_dict(ckpt['model']) model.eval() self.model = model
class Learner: def __init__(self, n_actors, device='cuda:0'): # params self.gamma = 0.99 self.alpha = 0.6 self.bootstrap_steps = 3 self.initial_exploration = 50000 self.priority_epsilon = 1e-6 self.device = device self.n_epochs = 0 self.n_actors = n_actors # path self.memory_path = os.path.join('./', 'logs', 'memory') self.net_path = os.path.join('./', 'logs', 'model', 'net.pt') self.target_net_path = os.path.join('./', 'logs', 'model', 'target_net.pt') # memory self.memory_size = 500000 self.batch_size = 128 self.memory_load_interval = 10 self.replay_memory = ReplayMemory(self.memory_size, self.batch_size, self.bootstrap_steps) # net self.net_save_interval = 50 self.target_update_interval = 1000 self.net = QNet(self.net_path, self.device).to(self.device) self.target_net = QNet(self.target_net_path, self.device).to(self.device) self.target_net.load_state_dict(self.net.state_dict()) self.net.save() self.target_net.save() self.optim = optim.RMSprop(self.net.parameters(), lr=0.00025 / 4.0, alpha=0.95, eps=1.5e-7, centered=True) def run(self): while True: if self.replay_memory.size > self.initial_exploration: self.train() self.interval() def train(self): batch, index, weights = self.replay_memory.sample(self.device) # q_value q_value = self.net(batch['state']) q_value = q_value.gather(1, batch['action']) # target q_value with torch.no_grad(): next_action = torch.argmax(self.net(batch["next_state"]), 1).view(-1, 1) next_q_value = self.target_net(batch["next_state"]).gather( 1, next_action) target_q_value = batch["reward"] + ( self.gamma** self.bootstrap_steps) * next_q_value * (1 - batch['done']) # update self.optim.zero_grad() loss = torch.mean(0.5 * (q_value - target_q_value)**2) loss.backward() self.optim.step() priority = (np.abs( (q_value - target_q_value).detach().cpu().numpy()).reshape(-1) + self.priority_epsilon)**self.alpha self.replay_memory.update_priority(index, priority) def interval(self): self.n_epochs += 1 if self.n_epochs % self.target_update_interval == 0: self.target_net.load_state_dict(self.net.state_dict()) if self.n_epochs % self.net_save_interval == 0: self.net.save() self.target_net.save() if self.n_epochs % self.memory_load_interval == 0: for i in range(self.n_actors): self.replay_memory.load(self.memory_path, i)
class Actor: def __init__(self, actor_id, n_actors, shared_dict, device='cpu'): # params self.gamma = 0.99 self.epsilon = 0.4 ** (1 + actor_id * 7 / (n_actors - 1)) self.bootstrap_steps = 3 self.alpha = 0.6 self.priority_epsilon = 1e-6 self.device = device self.actor_id = actor_id # path self.memory_path = os.path.join( './', 'logs', 'memory') # memory self.memory_size = 50000 self.batch_size = 32 self.action_repeat = 4 self.n_stacks = 4 self.burn_in_length = 10 self.learning_length = 10 self.overlap_length = 10 self.eta = 0.9 self.sequence_length = self.burn_in_length + self.learning_length self.stack_count = self.n_stacks // self.action_repeat self.memory_save_interval = 5 self.episode_start_index = 0 self.n_steps_memory = NStepMemory(self.bootstrap_steps, self.gamma) self.replay_memory = ReplayMemory(self.memory_size, self.batch_size, self.bootstrap_steps) # net self.shared_dict = shared_dict self.net_load_interval = 5 self.net = QNet(self.device).to(self.device) self.target_net = QNet(self.device).to(self.device) self.target_net.load_state_dict(self.net.state_dict()) # env self.env = PongEnv(self.action_repeat, self.n_stacks) self.episode_reward = 0 self.n_episodes = 0 self.n_steps = 0 self.memory_count = 0 self.state = self.env.reset() def run(self): while True: self.step() def step(self): state = self.state action, q_value, h, c, target_q_value, target_h, target_c = self.select_action(state) q_value = q_value.detach().cpu().numpy() target_q_value = target_q_value.detach().cpu().numpy() next_state, reward, done, _ = self.env.step(action) self.episode_reward += reward self.n_steps += 1 self.n_steps_memory.add(q_value, state[-self.action_repeat:], h, c, target_h, target_c, action, reward, self.stack_count) if self.stack_count > 1: self.stack_count -= 1 if self.n_steps > self.bootstrap_steps: pre_q_value, state, h, c, target_h, target_c, action, reward, stack_count = self.n_steps_memory.get() priority = self.calc_priority(pre_q_value, action, reward, q_value, target_q_value, done) self.replay_memory.add(state, h, c, target_h, target_c, action, reward, done, stack_count, priority) self.memory_count += 1 self.state = next_state.copy() if done: while self.n_steps_memory.size > 0: pre_q_value, state, h, c, target_h, target_c, action, reward, stack_count = self.n_steps_memory.get() priority = self.calc_priority(pre_q_value, action, reward, q_value, target_q_value, done) self.replay_memory.add(state, h, c, target_h, target_c, action, reward, done, stack_count, priority) self.memory_count += 1 self.reset() def select_action(self, state): state = torch.FloatTensor(state).unsqueeze(0).to(self.device) with torch.no_grad(): q_value, h, c = self.net(state, True) target_q_value, target_h, target_c = self.target_net(state, True) if np.random.random() < self.epsilon: action = np.random.randint(6) else: action = q_value.argmax().item() return action, q_value, h, c, target_q_value, target_h, target_c def reset(self): if self.n_episodes % 1 == 0: print('episodes:', self.n_episodes, 'actor_id:', self.actor_id, 'return:', self.episode_reward) self.net.reset() self.target_net.reset() self.set_seq_start_index() self.state = self.env.reset() self.episode_start_index = self.replay_memory.index self.episode_reward = 0 self.n_episodes += 1 self.n_steps = 0 self.memory_count = 0 self.stack_count = self.n_stacks // self.action_repeat # reset n_step memory self.n_steps_memory = NStepMemory(self.bootstrap_steps, self.gamma) # save replay memory if self.n_episodes % self.memory_save_interval == 0: self.replay_memory.save(self.memory_path, self.actor_id) self.replay_memory = ReplayMemory(self.memory_size, self.batch_size, self.bootstrap_steps) self.episode_start_index = 0 gc.collect() # load net if self.n_episodes % self.net_load_interval == 0: self.load_model() def load_model(self): try: self.net.load_state_dict(self.shared_dict['net_state']) self.target_net.load_state_dict(self.shared_dict['target_net_state']) except: print('load error') def calc_priority(self, q_value, action, reward, next_q_value, target_next_q_value, done): q_value = q_value.reshape(-1)[action] target_next_q_value = target_next_q_value.reshape(-1) if done: target_q_value = reward else: next_action = next_q_value.argmax(-1) target_next_q_value = target_next_q_value[next_action] target_q_value = reward + (self.gamma**self.bootstrap_steps) * target_next_q_value priority = np.abs(q_value - target_q_value) + self.priority_epsilon priority = priority ** self.alpha return priority def set_seq_start_index(self): last_index = self.replay_memory.index start_index = self.episode_start_index seq_start_index = [i for i in range(start_index, last_index-self.sequence_length, self.overlap_length)] seq_start_index.append(last_index - self.sequence_length) seq_start_index = np.array(seq_start_index) self.replay_memory.update_sequence_priority(seq_start_index) self.replay_memory.memory['is_seq_start'][seq_start_index] = 1
env = gym.make(args.env_name) env.seed(500) torch.manual_seed(500) state_size = env.observation_space.shape[0] action_size = env.action_space.n print('state size:', state_size) print('action size:', action_size) q_net = QNet(state_size, action_size, args) if args.load_model is not None: pretrained_model_path = os.path.join(os.getcwd(), 'save_model', str(args.load_model)) pretrained_model = torch.load(pretrained_model_path) q_net.load_state_dict(pretrained_model) steps = 0 for episode in range(args.iter): done = False score = 0 state = env.reset() state = np.reshape(state, [1, state_size]) while not done: if args.render: env.render() steps += 1
class QTDAgent(object): def __init__(self, state_dim, action_dim, learning_rate=0.001, reward_decay=0.99, e_greedy=0.9): self.action_dim = action_dim self.state_dim = state_dim self.lr = learning_rate self.gamma = reward_decay # in according to the parameters in the formulation. self.epsilon = e_greedy self.EPS_START = 0.9 self.EPS_END = 0.05 self.EPS_DECAY = 30000 # this decay is to slow. # TO DO: figure out the relationship between the decay and the totoal step. # try to use a good strategy to solve this problem. use_cuda = torch.cuda.is_available() self.LongTensor = torch.cuda.LongTensor if use_cuda else torch.LongTensor self.FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor self.model = QNet(self.state_dim, self.action_dim).cuda() if use_cuda else QNet( self.state_dim, self.action_dim) self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr) # self.scheduler = optim.StepLR(self.optimizer, step_size=10000, gamma=0.5) # the learning rate decrease by a factor gamma every 10000 step_size. util.weights_init(self.model) def sbc(self, v, volatile=False): return Variable(self.FloatTensor((np.expand_dims(v, 0).tolist())), volatile=volatile) def get_actions(self, state): action = self.model(self.sbc(state, volatile=True)) return action def select_action(self, state, steps_done): util.adjust_learning_rate(self.optimizer, self.lr, steps_done, 10000, lr_decay=0.2) # global steps_done sample = random.random() esp_threshold = self.EPS_END + (self.EPS_START - self.EPS_END) * \ np.exp(-1. * steps_done / self.EPS_DECAY) if sample > esp_threshold: actions = self.get_actions(state) action = actions.data.max(1)[1].view(1, 1) return action else: return self.LongTensor([[random.randrange(self.action_dim)]]) def update(self, pending): # def update(self, s, a, r, s_, a_,done=False): pending_len = len(pending) loss = 0 while (pending_len): pending_len = pending_len - 1 [s, a, r, s_, a_, done] = pending[pending_len] if (done == True): expect_state_action_value = r else: non_final_next_states = self.model(self.sbc(s_, volatile=True)) expect_state_action_value = r + self.gamma * non_final_next_states.max( 1)[0] expect_state_action_value.volatile = False # expect_state_action_value = r + self.gamma*self.model(Variable(torch.from_numpy(np.expand_dims(s_,0).astype('float32')))).max(1)[0] state_action_value = self.model(self.sbc(s))[0, a] loss += 0.5 * (state_action_value - expect_state_action_value).pow(2) self.optimizer.zero_grad() loss.backward() # loss.backward() # for param in self.model.parameters(): # param.grad.data.clamp_(-1,1) self.optimizer.step() def save_model(self, path): torch.save(self.model.state_dict(), '{}QTDAgent.pt'.format(path)) # torch.save(self.target_critic.state_dict(), '{}/critic.pt'.format(path)) print('Models saved successfully') def load_model(self, name): self.model.load_state_dict(name)
def train(render): online_net = QNet(h=84, w=84, outputs=36) online_net.load_state_dict(torch.load('saved/online_net.pt')) target_net = QNet(h=84, w=84, outputs=36) update_target_model(online_net, target_net) optimizer = optim.Adam(online_net.parameters(), lr=lr) online_net.to(device) target_net.to(device) online_net.train() target_net.train() memory = Memory(replay_memory_capacity) memory = torch.load('saved/model_memory.pt') epsilon = 0.1 steps = 0 beta = beta_start loss = 0 for e in range(100000): #level = random.choice(LEVEL_SET) level = 'Level01' env = make_retro(game=env_name, state=level, use_restricted_actions=retro.Actions.DISCRETE) done = False total_reward = 0.0 state = env.reset() state = torch.Tensor(state).to(device).permute(2, 0, 1) #state = state.view(state.size()[0], -1) state = state.unsqueeze(0) while not done: steps += 1 action = get_action(state.to(device), target_net, epsilon, env) if render: env.render() next_state, reward, done, info = env.step(action) next_state = torch.Tensor(next_state).permute(2, 0, 1) #next_state = next_state.view(next_state.size()[0], -1) next_state = next_state.unsqueeze(0) total_reward += reward mask = 0 if done else 1 action_one_hot = torch.zeros(36) action_one_hot[action] = 1 reward = torch.tensor([info['score']]).to(device) memory.push(state, next_state, action_one_hot, reward, mask) state = next_state if len(memory) > initial_exploration: epsilon -= 0.00005 epsilon = max(epsilon, 0.02) beta += 0.00005 beta = min(1, beta) batch, weights = memory.sample(batch_size, online_net, target_net, beta) loss = QNet.train_model(online_net, target_net, optimizer, batch, weights) if steps % update_target == 0: update_target_model(online_net, target_net) if e % 1 == 0: print('{} episode | Total Reward: {}'.format(e, total_reward)) torch.save(online_net.state_dict(), 'saved/online_net.pt') torch.save(memory, 'saved/model_memory.pt') env.close()
class Actor: def __init__(self, actor_id, n_actors, device='cpu'): # params self.gamma = 0.99 self.epsilon = 0.4**(1 + actor_id * 7 / (n_actors - 1)) self.bootstrap_steps = 3 self.alpha = 0.6 self.priority_epsilon = 1e-6 self.device = device self.actor_id = actor_id # path self.memory_path = os.path.join('./', 'logs', 'memory') self.net_path = os.path.join('./', 'logs', 'model', 'net.pt') self.target_net_path = os.path.join('./', 'logs', 'model', 'target_net.pt') # memory self.memory_size = 50000 self.batch_size = 32 self.action_repeat = 4 self.n_stacks = 4 self.stack_count = self.n_stacks // self.action_repeat self.memory_save_interval = 1 self.n_steps_memory = NStepMemory(self.bootstrap_steps, self.gamma) self.replay_memory = ReplayMemory(self.memory_size, self.batch_size, self.bootstrap_steps) # net self.net_load_interval = 5 self.net = QNet(self.net_path).to(self.device) self.target_net = QNet(self.target_net_path).to(self.device) self.target_net.load_state_dict(self.net.state_dict()) # env self.env = PongEnv(self.action_repeat, self.n_stacks) self.episode_reward = 0 self.n_episodes = 0 self.n_steps = 0 self.memory_count = 0 self.state = self.env.reset() def run(self): while True: self.step() def step(self): state = self.state action = self.select_action(state) next_state, reward, done, _ = self.env.step(action) self.episode_reward += reward self.n_steps += 1 self.n_steps_memory.add(state[-self.action_repeat:], action, reward, self.stack_count) if self.stack_count > 1: self.stack_count -= 1 if self.n_steps > self.bootstrap_steps: state, action, reward, stack_count = self.n_steps_memory.get() self.replay_memory.add(state, action, reward, done, stack_count) self.memory_count += 1 self.state = next_state.copy() if done: while self.n_steps_memory.size > 0: state, action, reward, stack_count = self.n_steps_memory.get() self.replay_memory.add(state, action, reward, done, stack_count) self.memory_count += 1 self.reset() def select_action(self, state): if np.random.random() < self.epsilon: action = np.random.randint(6) else: state = torch.FloatTensor(state).unsqueeze(0).to(self.device) with torch.no_grad(): q_val = self.net(state) action = q_val.argmax().item() return action def reset(self): if self.n_episodes % 1 == 0: print('episodes:', self.n_episodes, 'actor_id:', self.actor_id, 'return:', self.episode_reward) self.calc_priority() self.state = self.env.reset() self.episode_reward = 0 self.n_episodes += 1 self.n_steps = 0 self.memory_count = 0 self.stack_count = self.n_stacks // self.action_repeat # reset n_step memory self.n_steps_memory = NStepMemory(self.bootstrap_steps, self.gamma) # save replay memory if self.n_episodes % self.memory_save_interval == 0: self.replay_memory.save(self.memory_path, self.actor_id) self.replay_memory = ReplayMemory(self.memory_size, self.batch_size, self.bootstrap_steps) # load net if self.n_episodes % self.net_load_interval == 0: self.net.load() self.target_net.load() def calc_priority(self): last_index = self.replay_memory.size start_index = last_index - self.memory_count batch, index = self.replay_memory.indexing_sample( start_index, last_index, self.device) batch_size = batch['state'].shape[0] priority = np.zeros(batch_size, dtype=np.float32) mini_batch_size = 500 for start_index in range(0, batch_size, mini_batch_size): last_index = min(start_index + mini_batch_size, batch_size) mini_batch = dict() for key in batch.keys(): if key in ['reward', 'done']: mini_batch[key] = batch[key][start_index:last_index] else: mini_batch[key] = torch.tensor( batch[key][start_index:last_index]).to(self.device) mini_batch['action'] = mini_batch['action'].view(-1, 1).long() with torch.no_grad(): # q_value q_value = self.net(mini_batch['state']).gather( 1, mini_batch['action']).view(-1, 1).cpu().numpy() # taget_q_value next_action = torch.argmax(self.net(mini_batch['next_state']), 1).view(-1, 1) next_q_value = self.target_net( mini_batch['next_state']).gather( 1, next_action).cpu().numpy() target_q_value = mini_batch['reward'] + ( self.gamma** self.bootstrap_steps) * next_q_value * (1 - mini_batch['done']) delta = np.abs(q_value - target_q_value).reshape(-1) + self.priority_epsilon delta = delta**self.alpha priority[start_index:last_index] = delta self.replay_memory.update_priority(index, priority)
def test(level_list, render=True): online_net = QNet(h=84, w=84, outputs=36) online_net.load_state_dict(torch.load('saved/online_net.pt')) online_net.to(device) cnt = 0 death = 0 total_reward = 0.0 str_level_list = [LEVEL_SET[idx - 1] for idx in level_list] for level in str_level_list: env = make_retro(game=env_name, state=level, use_restricted_actions=retro.Actions.DISCRETE) obs = env.reset() state = torch.Tensor(obs).to(device).permute(2, 0, 1) #state = state.view(state.size()[0], -1) state = state.unsqueeze(0) previous_lives = 3 previous_level = level_list[cnt] cnt += 1 if death >= 3: break for t in count(): action = online_net.get_action(state.to(device)) if render: env.render() time.sleep(0.02) next_state, reward, done, info = env.step(action) next_state = torch.Tensor(next_state).permute(2, 0, 1) #next_state = next_state.view(next_state.size()[0], -1) next_state = next_state.unsqueeze(0) total_reward += reward current_lives = info['lives'] current_level = info['level'] if current_lives != previous_lives: print('Dead') previous_lives = info['lives'] death += 1 #if death >= 3: # print("Finished ", level, " Total reward: {}".format(total_reward)) # break if current_level != previous_level: print('Stage changed') print("Finished ", level, " Total reward: {}".format(total_reward)) break state = next_state if done: print('All lives gone') print("Finished ", level, " Total reward: {}".format(total_reward)) break env.close() return
optimizer.step() return loss # Build environment env = make_atari('PongNoFrameskip-v4', stack=2) env = wrap_pytorch(env) number_actions = env.action_space.n replay_buffer = ReplayBuffer(replay_memory_size) # Separate target net & policy net input_shape = env.reset().shape current_net = QNet(input_shape, number_actions).to(device) target_net = QNet(input_shape, number_actions).to(device) # with older weights target_net.load_state_dict(current_net.state_dict()) target_net.eval() optimizer = opt_algorithm(current_net.parameters(), lr=learning_rate) n_episode = 1 episode_return = 0 best_return = 0 returns = [] state = env.reset() for i in count(): # env.render() eps = get_epsilon(i) action = select_action(state, current_net, eps, number_action=number_actions)
start_epoch = 1 if args.resume: ckpt_dir_r = os.path.join(args.resume, 'ckpt') ckpts = glob2.glob(os.path.join(ckpt_dir_r, '*.pth')) assert ckpts, "No checkpoints to resume from!" def get_epoch(ckpt_url): s = re.findall("ckpt_e(\d+).pth", ckpt_url) epoch = int(s[0]) if s else -1 return epoch, ckpt_url start_epoch, ckpt = max(get_epoch(c) for c in ckpts) print('Checkpoint:', ckpt) ckpt = torch.load(ckpt) model.load_state_dict(ckpt['model']) start_epoch = ckpt['epoch'] best_mse = ckpt['mse_val'] start_epoch = ckpt['epoch'] for epoch in trange(start_epoch, args.epochs + 1): cur_loss = trainEval(train_loader, model, optimizer, args, True) val_loss = trainEval(val_loader, model, optimizer, args, False) test_loss = trainEval(test_loader, model, optimizer, args, False) metrics = {'epoch': epoch} metrics['mse_train'] = cur_loss metrics['mse_val'] = val_loss metrics['mse_test'] = test_loss log = log.append(metrics, ignore_index=True) log.to_csv(log_file, index=False)
class Learner: def __init__(self, n_actors, shared_dict, device='cuda:0'): # params self.gamma = 0.99 self.alpha = 0.6 self.bootstrap_steps = 3 self.initial_exploration = 50000 self.priority_epsilon = 1e-6 self.device = device self.n_epochs = 0 self.n_actors = n_actors # path self.memory_path = os.path.join('./', 'logs', 'memory') # memory self.burn_in_length = 10 self.learning_length = 10 self.sequence_length = self.burn_in_length + self.learning_length self.memory_size = 500000 self.batch_size = 8 self.memory_load_interval = 20 self.replay_memory = ReplayMemory(self.memory_size, self.batch_size, self.bootstrap_steps) # net self.shared_dict = shared_dict self.net_save_interval = 100 self.target_update_interval = 1000 self.net = QNet(self.device).to(self.device) self.target_net = QNet(self.device).to(self.device) self.target_net.load_state_dict(self.net.state_dict()) self.save_model() self.optim = optim.RMSprop(self.net.parameters(), lr=0.00025 / 4.0, alpha=0.95, eps=1.5e-7, centered=True) def run(self): while True: if self.replay_memory.size > self.initial_exploration: self.train() if self.n_epochs % 100 == 0: print('trained', self.n_epochs, 'epochs') self.interval() def train(self): batch, seq_index, index = self.replay_memory.sample(self.device) self.net.set_state(batch['hs'], batch['cs']) self.target_net.set_state(batch['target_hs'], batch['target_cs']) ### burn-in step ### state = batch['state'][:self.burn_in_length] next_state = batch['next_state'][:self.burn_in_length] with torch.no_grad(): _ = self.net(state) _ = self.target_net(next_state) ### learning step ### state = batch['state'][self.burn_in_length:] next_state = batch['next_state'][self.burn_in_length:] # q_value q_value = self.net(state).gather(1, batch['action'].view(-1, 1)) # target q_value with torch.no_grad(): next_action = torch.argmax(self.net(next_state), 1).view(-1, 1) next_q_value = self.target_net(next_state).gather(1, next_action) target_q_value = batch["reward"].view( -1, 1) + (self.gamma**self.bootstrap_steps) * next_q_value * ( 1 - batch['done'].view(-1, 1)) # update self.optim.zero_grad() loss = torch.mean(0.5 * (q_value - target_q_value)**2) loss.backward() self.optim.step() priority = (np.abs( (q_value - target_q_value).detach().cpu().numpy()).reshape(-1) + self.priority_epsilon)**self.alpha self.replay_memory.update_priority( index[self.burn_in_length:].reshape(-1), priority) self.replay_memory.update_sequence_priority(seq_index, True) def interval(self): self.n_epochs += 1 if self.n_epochs % self.target_update_interval == 0: self.target_net.load_state_dict(self.net.state_dict()) if self.n_epochs % self.net_save_interval == 0: self.save_model() if self.n_epochs % self.memory_load_interval == 0: for i in range(self.n_actors): self.replay_memory.load(self.memory_path, i) def save_model(self): self.shared_dict['net_state'] = deepcopy(self.net).cpu().state_dict() self.shared_dict['target_net_state'] = deepcopy( self.target_net).cpu().state_dict()
else: input = torch.from_numpy(state).to(device, torch.float32).unsqueeze(0) score = net(input) action = score.max(dim=1)[1].to(torch.int64).item() return action # Build environment env = make_atari('PongNoFrameskip-v4', stack=2) env = wrap_pytorch(env) env = gym.wrappers.Monitor(env, directory='./movie', force=True, video_callable=lambda x: True) number_actions = env.action_space.n # Separate target net & policy net input_shape = env.reset().shape net = QNet(input_shape, number_actions) net.load_state_dict(torch.load(model)) net.eval().to(device) for episode in range(10): state = env.reset() done = False while not done: # env.render() action = select_action(state, number_actions=number_actions) next_state, reward, done, _ = env.step(action) state = next_state env.close()