def eval_policy(args, env):
action_space = env.action_space.n
print("show action space", action_space)
# Agent
dqn = Agent(args, env)
size = 84
episode_reward = 0
eps = 0.1
for episode in range(2):
print("Episode ", episode)
state = env.reset()
state = torch.tensor(state, dtype=torch.float32,
device=args.device).div_(255)
zeros = torch.zeros_like(state)
state_buffer = deque([], maxlen=args.history_length)
state_buffer.append(zeros)
state_buffer.append(zeros)
state_buffer.append(zeros)
state_buffer.append(state)
state = torch.stack(list(state_buffer), 0)
for step in range(2):
action = dqn.act_e_greedy(
state, eps) # Choose an action greedily (with noisy weights)
next_state, reward, done, _ = env.step(action)
print(reward)
episode_reward += reward
if step == 39:
done = True
next_state = cv2.resize(next_state, (size, size),
interpolation=cv2.INTER_LINEAR)
next_state = torch.tensor(next_state,
dtype=torch.float32,
device=args.device).div_(255)
state_buffer.append(next_state)
state = torch.stack(list(state_buffer), 0)
print("Epiosde reward ", episode_reward)
state = cv2.resize(state[:, :, 0], (84, 84),
interpolation=cv2.INTER_LINEAR)
state = torch.tensor(state,
dtype=torch.float32,
device=args.device).div_(255)
zeros = torch.zeros_like(state)
state_buffer = deque([], maxlen=args.history_length)
state_buffer.append(zeros)
state_buffer.append(zeros)
state_buffer.append(zeros)
state_buffer.append(state)
state = torch.stack(list(state_buffer), 0)
print(state.shape)
if step < 10:
action = np.random.randint(0, action_space)
else:
action = dqn.act_e_greedy(state) # Choose an action greedily
print("action", action)
state, reward, done, _ = env.step(action) # Step
state = cv2.resize(state[:, :, 0], (84, 84),
interpolation=cv2.INTER_LINEAR)
state = torch.tensor(state, dtype=torch.float32,
device=args.device).div_(255)
state_buffer.append(state)
state = torch.stack(list(state_buffer), 0)
reward_sum += reward
print(" episode reward", reward_sum)
T_rewards.append(reward_sum)
print(T_rewards)
dqn.eval() # Set DQN (online network) to evaluation mode
avg_reward, avg_Q = test(args, 0, dqn, val_mem, evaluate=True) # Test
print('Avg. reward: ' + str(avg_reward) + ' | Avg. Q: ' + str(avg_Q))
else:
# Training loop
#print('Training')
dqn.train()
T, done = 0, True
for T in tqdm(range(args.T_max)):
if done:
state, done = env.reset(), False
#print('replay_frequency')
if T % args.replay_frequency == 0:
dqn.reset_noise() # Draw a new set of noisy weights
action = dqn.act_e_greedy(state) # Choose an action greedily (with noisy weights)
next_state, reward, done = env.step(action) # Step
#print('reward_clip')
if args.reward_clip > 0:
reward = max(min(reward, args.reward_clip), -args.reward_clip) # Clip rewards
mem.append(state, action, reward, done) # Append transition to memory
T += 1
# Train and test
if T >= args.learn_start:
#print('learn_start')
mem.priority_weight = min(mem.priority_weight + priority_weight_increase, 1) # Anneal importance sampling weight β to 1
#print('replay_frequency')
if T % args.replay_frequency == 0:
def worker(gpu, ngpus_per_node, args):
args.gpu = gpu
if args.distributed:
args.seed += args.gpu
torch.cuda.set_device(args.gpu)
args.rank = int(os.environ['RANK']) if 'RANK' in os.environ else 0
if args.multiprocessing_distributed:
args.rank = args.rank * ngpus_per_node + args.gpu
torch.distributed.init_process_group(backend='nccl', init_method='tcp://127.0.0.1:8632',
world_size=args.world_size, rank=args.rank)
else:
args.rank = 0
args.use_cuda_env = args.use_cuda_env and torch.cuda.is_available()
args.no_cuda_train = not torch.cuda.is_available()
args.verbose = args.verbose and (args.rank == 0)
env_device = torch.device('cuda', args.gpu) if args.use_cuda_env else torch.device('cpu')
train_device = torch.device('cuda', args.gpu) if (args.no_cuda_train == False) else torch.device('cpu')
# Setup
np.random.seed(args.seed)
torch.manual_seed(np.random.randint(1, 10000))
if args.use_cuda_env or (args.no_cuda_train == False):
torch.cuda.manual_seed(random.randint(1, 10000))
if train_device.type == 'cuda':
print('Train:\n' + cuda_device_str(train_device.index), flush=True)
if args.use_openai:
test_env = create_vectorize_atari_env(args.env_name, args.seed, args.evaluation_episodes,
episode_life=False, clip_rewards=False)
test_env.reset()
else:
test_env = AtariEnv(args.env_name, args.evaluation_episodes, color_mode='gray',
device='cpu', rescale=True, clip_rewards=False,
episodic_life=False, repeat_prob=0.0, frameskip=4)
# Agent
dqn = Agent(args, test_env.action_space)
# Construct validation memory
if args.rank == 0:
print(dqn)
print('Initializing evaluation memory with {} entries...'.format(args.evaluation_size), end='', flush=True)
start_time = time.time()
val_mem = initialize_validation(args, train_device)
if args.rank == 0:
print('complete ({})'.format(format_time(time.time() - start_time)), flush=True)
if args.evaluate:
if args.rank == 0:
eval_start_time = time.time()
dqn.eval() # Set DQN (online network) to evaluation mode
rewards, lengths, avg_Q = test(args, 0, dqn, val_mem, test_env, train_device)
dqn.train() # Set DQN (online network) back to training mode
eval_total_time = time.time() - eval_start_time
rmean, rmedian, rstd, rmin, rmax = vec_stats(rewards)
lmean, lmedian, lstd, lmin, lmax = vec_stats(lengths)
print('reward: {:4.2f}, {:4.0f}, {:4.0f}, {:4.4f} | '
'length: {:4.2f}, {:4.0f}, {:4.0f}, {:4.4f} | '
'Avg. Q: {:4.4f} | {}'
.format(rmean, rmin, rmax, rstd, lmean, lmin, lmax,
lstd, avg_Q, format_time(eval_total_time)),
flush=True)
else:
if args.rank == 0:
print('Entering main training loop', flush=True)
if args.output_filename:
csv_file = open(args.output_filename, 'w', newline='')
csv_file.write(json.dumps(vars(args)))
csv_file.write('\n')
csv_writer = csv.writer(csv_file, delimiter=',')
csv_writer.writerow(['frames', 'total_time',
'rmean', 'rmedian', 'rstd', 'rmin', 'rmax',
'lmean', 'lmedian', 'lstd', 'lmin', 'lmax'])
else:
csv_writer, csv_file = None, None
if args.plot:
from tensorboardX import SummaryWriter
current_time = datetime.now().strftime('%b%d_%H-%M-%S')
log_dir = os.path.join(args.log_dir, current_time + '_' + socket.gethostname())
writer = SummaryWriter(log_dir=log_dir)
for k, v in vars(args).items():
writer.add_text(k, str(v))
# Environment
print('Initializing environments...', end='', flush=True)
start_time = time.time()
if args.use_openai:
train_env = create_vectorize_atari_env(args.env_name, args.seed, args.num_ales,
episode_life=True, clip_rewards=args.reward_clip,
max_frames=args.max_episode_length)
observation = torch.from_numpy(train_env.reset()).squeeze(1)
else:
train_env = AtariEnv(args.env_name, args.num_ales, color_mode='gray',
device=env_device, rescale=True,
clip_rewards=args.reward_clip,
episodic_life=True, repeat_prob=0.0)
train_env.train()
observation = train_env.reset(initial_steps=args.ale_start_steps, verbose=args.verbose).clone().squeeze(-1)
if args.rank == 0:
print('complete ({})'.format(format_time(time.time() - start_time)), flush=True)
# These variables are used to compute average rewards for all processes.
episode_rewards = torch.zeros(args.num_ales, device=train_device, dtype=torch.float32)
episode_lengths = torch.zeros(args.num_ales, device=train_device, dtype=torch.float32)
final_rewards = torch.zeros(args.num_ales, device=train_device, dtype=torch.float32)
final_lengths = torch.zeros(args.num_ales, device=train_device, dtype=torch.float32)
has_completed = torch.zeros(args.num_ales, device=train_device, dtype=torch.bool)
mem = ReplayMemory(args, args.memory_capacity, train_device)
mem.reset(observation)
priority_weight_increase = (1 - args.priority_weight) / (args.t_max - args.learn_start)
state = torch.zeros((args.num_ales, args.history_length, 84, 84), device=mem.device, dtype=torch.float32)
state[:, -1] = observation.to(device=mem.device, dtype=torch.float32).div(255.0)
num_frames_per_iter = args.num_ales
total_steps = math.ceil(args.t_max / (args.world_size * num_frames_per_iter))
epsilons = np.linspace(args.epsilon_start, args.epsilon_final, math.ceil(args.epsilon_frames / num_frames_per_iter))
epsilon_offset = math.ceil(args.learn_start / num_frames_per_iter)
prefetcher = data_prefetcher(args.batch_size, train_device, mem)
avg_loss = 'N/A'
eval_offset = 0
target_update_offset = 0
total_time = 0
# main loop
iterator = range(total_steps)
if args.rank == 0:
iterator = tqdm(iterator)
env_stream = torch.cuda.Stream()
train_stream = torch.cuda.Stream()
for update in iterator:
T = args.world_size * update * num_frames_per_iter
epsilon = epsilons[min(update - epsilon_offset, len(epsilons) - 1)] if T >= args.learn_start else epsilons[0]
start_time = time.time()
if update % args.replay_frequency == 0:
dqn.reset_noise() # Draw a new set of noisy weights
dqn.eval()
nvtx.range_push('train:select action')
if args.noisy_linear:
action = dqn.act(state) # Choose an action greedily (with noisy weights)
else:
action = dqn.act_e_greedy(state, epsilon=epsilon)
nvtx.range_pop()
dqn.train()
if args.use_openai:
action = action.cpu().numpy()
torch.cuda.synchronize()
with torch.cuda.stream(env_stream):
nvtx.range_push('train:env step')
observation, reward, done, info = train_env.step(action) # Step
if args.use_openai:
# convert back to pytorch tensors
observation = torch.from_numpy(observation).squeeze(1)
reward = torch.from_numpy(reward.astype(np.float32))
done = torch.from_numpy(done.astype(np.bool))
action = torch.from_numpy(action)
else:
observation = observation.clone().squeeze(-1)
nvtx.range_pop()
observation = observation.to(device=train_device)
reward = reward.to(device=train_device)
done = done.to(device=train_device, dtype=torch.bool)
action = action.to(device=train_device)
observation = observation.float().div_(255.0)
not_done = 1.0 - done.float()
state[:, :-1].copy_(state[:, 1:].clone())
state *= not_done.view(-1, 1, 1, 1)
state[:, -1].copy_(observation)
# update episodic reward counters
has_completed |= done
episode_rewards += reward.float()
final_rewards[done] = episode_rewards[done]
episode_rewards *= not_done
episode_lengths += not_done
final_lengths[done] = episode_lengths[done]
episode_lengths *= not_done
# Train and test
if T >= args.learn_start:
mem.priority_weight = min(mem.priority_weight + priority_weight_increase, 1) # Anneal importance sampling weight β to 1
prefetcher.preload()
avg_loss = 0.0
num_minibatches = min(int(args.num_ales / args.replay_frequency), 8)
for _ in range(num_minibatches):
# Sample transitions
nvtx.range_push('train:sample states')
idxs, states, actions, returns, next_states, nonterminals, weights = prefetcher.next()
nvtx.range_pop()
nvtx.range_push('train:network update')
loss = dqn.learn(states, actions, returns, next_states, nonterminals, weights)
nvtx.range_pop()
nvtx.range_push('train:update priorities')
mem.update_priorities(idxs, loss) # Update priorities of sampled transitions
nvtx.range_pop()
avg_loss += loss.mean().item()
avg_loss /= num_minibatches
# Update target network
if T >= target_update_offset:
dqn.update_target_net()
target_update_offset += args.target_update
torch.cuda.current_stream().wait_stream(env_stream)
torch.cuda.current_stream().wait_stream(train_stream)
nvtx.range_push('train:append memory')
mem.append(observation, action, reward, done) # Append transition to memory
nvtx.range_pop()
total_time += time.time() - start_time
if args.rank == 0:
if args.plot and ((update % args.replay_frequency) == 0):
writer.add_scalar('train/epsilon', epsilon, T)
writer.add_scalar('train/rewards', final_rewards.mean(), T)
writer.add_scalar('train/lengths', final_lengths.mean(), T)
if T >= eval_offset:
eval_start_time = time.time()
dqn.eval() # Set DQN (online network) to evaluation mode
rewards, lengths, avg_Q = test(args, T, dqn, val_mem, test_env, train_device)
dqn.train() # Set DQN (online network) back to training mode
eval_total_time = time.time() - eval_start_time
eval_offset += args.evaluation_interval
rmean, rmedian, rstd, rmin, rmax = vec_stats(rewards)
lmean, lmedian, lstd, lmin, lmax = vec_stats(lengths)
print('reward: {:4.2f}, {:4.0f}, {:4.0f}, {:4.4f} | '
'length: {:4.2f}, {:4.0f}, {:4.0f}, {:4.4f} | '
'Avg. Q: {:4.4f} | {}'
.format(rmean, rmin, rmax, rstd, lmean, lmin, lmax,
lstd, avg_Q, format_time(eval_total_time)),
flush=True)
if args.output_filename and csv_writer and csv_file:
csv_writer.writerow([T, total_time,
rmean, rmedian, rstd, rmin, rmax,
lmean, lmedian, lstd, lmin, lmax])
csv_file.flush()
if args.plot:
writer.add_scalar('eval/rewards', rmean, T)
writer.add_scalar('eval/lengths', lmean, T)
writer.add_scalar('eval/avg_Q', avg_Q, T)
loss_str = '{:4.4f}'.format(avg_loss) if isinstance(avg_loss, float) else avg_loss
progress_data = 'T = {:,} epsilon = {:4.2f} avg reward = {:4.2f} loss: {}' \
.format(T, epsilon, final_rewards.mean().item(), loss_str)
iterator.set_postfix_str(progress_data)
if args.plot and (args.rank == 0):
writer.close()
if args.use_openai:
train_env.close()
test_env.close()
last_done_T = T
if T % args.replay_frequency == 0:
dqn.reset_noise() # Draw a new set of noisy weights
if args.explore_eps is None:
action = dqn.act(
state) # Choose an action greedily (with noisy weights)
else:
init_eps = 1
decay_step = args.explore_eps[0] if args.explore_eps[
0] > 1 else args.explore_eps[0] * args.T_max
final_eps = args.explore_eps[1]
eps = max(init_eps - (init_eps - final_eps) / decay_step * T,
final_eps)
action = dqn.act_e_greedy(state, eps)
next_state, reward, done, _ = env.step(action) # Step
episode_reward += reward
episode_length += 1
if args.count_base_bonus > 0:
if args.deploy_policy == "info-matrix":
info_index = (T - last_done_T) // args.info_matrix_interval
reward = reward + args.count_base_bonus / math.sqrt(
hash_table.step(
state, action, T > args.learn_start
and not visited_deploy_flag, True, info_index))
else:
reward = reward + args.count_base_bonus / math.sqrt(
hash_table.step(
state, action, T > args.learn_start
and not visited_deploy_flag))
