help="the default config directory")
parser.add_argument("-v",
"--verbose",
help="increase output verbosity",
action="store_true")
parser.add_argument("-vv",
"--very-verbose",
help="print out warnings",
action="store_true")
args = parser.parse_args()
if args.very_verbose:
args.verbose = args.very_verbose
warnings.simplefilter("default", textworld.TextworldGenerationWarning)
# Read config from yaml file.
config_file = pjoin(args.config_dir, 'config.yaml')
with open(config_file) as reader:
config = yaml.safe_load(reader)
default_logs_path = get_experiment_dir(config)
setup_logging(default_config_path=pjoin(args.config_dir,
'logging_config.yaml'),
default_level=logging.INFO,
add_time_stamp=True,
default_logs_path=default_logs_path)
log_git_commit(logger)
r, s = test(config=config)
print("$$$", r, s)
def train(config):
# train env
print('Setting up TextWorld environment...')
batch_size = config['training']['scheduling']['batch_size']
env_id = gym_textworld.make_batch(env_id=config['general']['env_id'],
batch_size=batch_size,
parallel=True)
env = gym.make(env_id)
env.seed(config['general']['random_seed'])
# valid and test env
run_test = config['general']['run_test']
if run_test:
test_batch_size = config['training']['scheduling']['test_batch_size']
# valid
valid_env_name = config['general']['valid_env_id']
valid_env_id = gym_textworld.make_batch(env_id=valid_env_name,
batch_size=test_batch_size,
parallel=True)
valid_env = gym.make(valid_env_id)
valid_env.seed(config['general']['random_seed'])
# test
test_env_name_list = config['general']['test_env_id']
assert isinstance(test_env_name_list, list)
test_env_id_list = [
gym_textworld.make_batch(env_id=item,
batch_size=test_batch_size,
parallel=True)
for item in test_env_name_list
]
test_env_list = [
gym.make(test_env_id) for test_env_id in test_env_id_list
]
for i in range(len(test_env_list)):
test_env_list[i].seed(config['general']['random_seed'])
print('Done.')
# Set the random seed manually for reproducibility.
np.random.seed(config['general']['random_seed'])
torch.manual_seed(config['general']['random_seed'])
if torch.cuda.is_available():
if not config['general']['use_cuda']:
logger.warning(
"WARNING: CUDA device detected but 'use_cuda: false' found in config.yaml"
)
else:
torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed(config['general']['random_seed'])
else:
config['general']['use_cuda'] = False # Disable CUDA.
revisit_counting = config['general']['revisit_counting']
replay_batch_size = config['general']['replay_batch_size']
replay_memory_capacity = config['general']['replay_memory_capacity']
replay_memory_priority_fraction = config['general'][
'replay_memory_priority_fraction']
word_vocab = dict2list(env.observation_space.id2w)
word2id = {}
for i, w in enumerate(word_vocab):
word2id[w] = i
# collect all nouns
verb_list = ["go", "take"]
object_name_list = ["east", "west", "north", "south", "coin"]
verb_map = [word2id[w] for w in verb_list if w in word2id]
noun_map = [word2id[w] for w in object_name_list if w in word2id]
agent = RLAgent(
config,
word_vocab,
verb_map,
noun_map,
replay_memory_capacity=replay_memory_capacity,
replay_memory_priority_fraction=replay_memory_priority_fraction)
init_learning_rate = config['training']['optimizer']['learning_rate']
exp_dir = get_experiment_dir(config)
summary = SummaryWriter(exp_dir)
parameters = filter(lambda p: p.requires_grad, agent.model.parameters())
if config['training']['optimizer']['step_rule'] == 'sgd':
optimizer = torch.optim.SGD(parameters, lr=init_learning_rate)
elif config['training']['optimizer']['step_rule'] == 'adam':
optimizer = torch.optim.Adam(parameters, lr=init_learning_rate)
log_every = 100
reward_avg = SlidingAverage('reward avg', steps=log_every)
step_avg = SlidingAverage('step avg', steps=log_every)
loss_avg = SlidingAverage('loss avg', steps=log_every)
# save & reload checkpoint only in 0th agent
best_avg_reward = -10000
best_avg_step = 10000
# step penalty
discount_gamma = config['general']['discount_gamma']
provide_prev_action = config['general']['provide_prev_action']
# epsilon greedy
epsilon_anneal_epochs = config['general']['epsilon_anneal_epochs']
epsilon_anneal_from = config['general']['epsilon_anneal_from']
epsilon_anneal_to = config['general']['epsilon_anneal_to']
# counting reward
revisit_counting_lambda_anneal_epochs = config['general'][
'revisit_counting_lambda_anneal_epochs']
revisit_counting_lambda_anneal_from = config['general'][
'revisit_counting_lambda_anneal_from']
revisit_counting_lambda_anneal_to = config['general'][
'revisit_counting_lambda_anneal_to']
epsilon = epsilon_anneal_from
revisit_counting_lambda = revisit_counting_lambda_anneal_from
for epoch in range(config['training']['scheduling']['epoch']):
agent.model.train()
obs, infos = env.reset()
agent.reset(infos)
print_command_string, print_rewards = [[] for _ in infos
], [[] for _ in infos]
print_interm_rewards = [[] for _ in infos]
print_rc_rewards = [[] for _ in infos]
dones = [False] * batch_size
rewards = None
avg_loss_in_this_game = []
new_observation_strings = agent.get_observation_strings(infos)
if revisit_counting:
agent.reset_binarized_counter(batch_size)
revisit_counting_rewards = agent.get_binarized_count(
new_observation_strings)
current_game_step = 0
prev_actions = ["" for _ in range(batch_size)
] if provide_prev_action else None
input_description, description_id_list = agent.get_game_step_info(
obs, infos, prev_actions)
while not all(dones):
v_idx, n_idx, chosen_strings, state_representation = agent.generate_one_command(
input_description, epsilon=epsilon)
obs, rewards, dones, infos = env.step(chosen_strings)
new_observation_strings = agent.get_observation_strings(infos)
if provide_prev_action:
prev_actions = chosen_strings
# counting
if revisit_counting:
revisit_counting_rewards = agent.get_binarized_count(
new_observation_strings, update=True)
else:
revisit_counting_rewards = [0.0 for _ in range(batch_size)]
agent.revisit_counting_rewards.append(revisit_counting_rewards)
revisit_counting_rewards = [
float(format(item, ".3f")) for item in revisit_counting_rewards
]
for i in range(len(infos)):
print_command_string[i].append(chosen_strings[i])
print_rewards[i].append(rewards[i])
print_interm_rewards[i].append(infos[i]["intermediate_reward"])
print_rc_rewards[i].append(revisit_counting_rewards[i])
if type(dones) is bool:
dones = [dones] * batch_size
agent.rewards.append(rewards)
agent.dones.append(dones)
agent.intermediate_rewards.append(
[info["intermediate_reward"] for info in infos])
# computer rewards, and push into replay memory
rewards_np, rewards, mask_np, mask = agent.compute_reward(
revisit_counting_lambda=revisit_counting_lambda,
revisit_counting=revisit_counting)
curr_description_id_list = description_id_list
input_description, description_id_list = agent.get_game_step_info(
obs, infos, prev_actions)
for b in range(batch_size):
if mask_np[b] == 0:
continue
if replay_memory_priority_fraction == 0.0:
# vanilla replay memory
agent.replay_memory.push(curr_description_id_list[b],
v_idx[b], n_idx[b], rewards[b],
mask[b], dones[b],
description_id_list[b],
new_observation_strings[b])
else:
# prioritized replay memory
is_prior = rewards_np[b] > 0.0
agent.replay_memory.push(is_prior,
curr_description_id_list[b],
v_idx[b], n_idx[b], rewards[b],
mask[b], dones[b],
description_id_list[b],
new_observation_strings[b])
if current_game_step > 0 and current_game_step % config["general"][
"update_per_k_game_steps"] == 0:
policy_loss = agent.update(replay_batch_size,
discount_gamma=discount_gamma)
if policy_loss is None:
continue
loss = policy_loss
# Backpropagate
optimizer.zero_grad()
loss.backward(retain_graph=True)
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm(
agent.model.parameters(),
config['training']['optimizer']['clip_grad_norm'])
optimizer.step() # apply gradients
avg_loss_in_this_game.append(to_np(policy_loss))
current_game_step += 1
agent.finish()
avg_loss_in_this_game = np.mean(avg_loss_in_this_game)
reward_avg.add(agent.final_rewards.mean())
step_avg.add(agent.step_used_before_done.mean())
loss_avg.add(avg_loss_in_this_game)
# annealing
if epoch < epsilon_anneal_epochs:
epsilon -= (epsilon_anneal_from -
epsilon_anneal_to) / float(epsilon_anneal_epochs)
if epoch < revisit_counting_lambda_anneal_epochs:
revisit_counting_lambda -= (
revisit_counting_lambda_anneal_from -
revisit_counting_lambda_anneal_to
) / float(revisit_counting_lambda_anneal_epochs)
# Tensorboard logging #
# (1) Log some numbers
if (epoch + 1
) % config["training"]["scheduling"]["logging_frequency"] == 0:
summary.add_scalar('avg_reward', reward_avg.value, epoch + 1)
summary.add_scalar('curr_reward', agent.final_rewards.mean(),
epoch + 1)
summary.add_scalar('curr_interm_reward',
agent.final_intermediate_rewards.mean(),
epoch + 1)
summary.add_scalar('curr_counting_reward',
agent.final_counting_rewards.mean(), epoch + 1)
summary.add_scalar('avg_step', step_avg.value, epoch + 1)
summary.add_scalar('curr_step', agent.step_used_before_done.mean(),
epoch + 1)
summary.add_scalar('loss_avg', loss_avg.value, epoch + 1)
summary.add_scalar('curr_loss', avg_loss_in_this_game, epoch + 1)
msg = 'E#{:03d}, R={:.3f}/{:.3f}/IR{:.3f}/CR{:.3f}, S={:.3f}/{:.3f}, L={:.3f}/{:.3f}, epsilon={:.4f}, lambda_counting={:.4f}'
msg = msg.format(epoch, np.mean(reward_avg.value),
agent.final_rewards.mean(),
agent.final_intermediate_rewards.mean(),
agent.final_counting_rewards.mean(),
np.mean(step_avg.value),
agent.step_used_before_done.mean(),
np.mean(loss_avg.value), avg_loss_in_this_game,
epsilon, revisit_counting_lambda)
if (epoch + 1
) % config["training"]["scheduling"]["logging_frequency"] == 0:
print("=========================================================")
for prt_cmd, prt_rew, prt_int_rew, prt_rc_rew in zip(
print_command_string, print_rewards, print_interm_rewards,
print_rc_rewards):
print("------------------------------")
print(prt_cmd)
print(prt_rew)
print(prt_int_rew)
print(prt_rc_rew)
print(msg)
# test on a different set of games
if run_test and (epoch + 1) % config["training"]["scheduling"][
"logging_frequency"] == 0:
valid_R, valid_IR, valid_S = test(config, valid_env, agent,
test_batch_size, word2id)
summary.add_scalar('valid_reward', valid_R, epoch + 1)
summary.add_scalar('valid_interm_reward', valid_IR, epoch + 1)
summary.add_scalar('valid_step', valid_S, epoch + 1)
# save & reload checkpoint by best valid performance
model_checkpoint_path = config['training']['scheduling'][
'model_checkpoint_path']
if valid_R > best_avg_reward or (valid_R == best_avg_reward
and valid_S < best_avg_step):
best_avg_reward = valid_R
best_avg_step = valid_S
torch.save(agent.model.state_dict(), model_checkpoint_path)
print("========= saved checkpoint =========")
for test_id in range(len(test_env_list)):
R, IR, S = test(config, test_env_list[test_id], agent,
test_batch_size, word2id)
summary.add_scalar('test_reward_' + str(test_id), R,
epoch + 1)
summary.add_scalar('test_interm_reward_' + str(test_id),
IR, epoch + 1)
summary.add_scalar('test_step_' + str(test_id), S,
epoch + 1)