def __init__(self, config, args, threshold=0.3):
self.bs_obj = CREST(threshold=threshold)
self.config = config
validation_games = 20
teacher_path = config['general']['teacher_model_path']
print('Setting up TextWorld environment...')
self.batch_size = 1
# load
print('Making env id {}'.format(config['general']['env_id']))
env_id = gym_textworld.make_batch(env_id=config['general']['env_id'],
batch_size=self.batch_size,
parallel=True)
self.env = gym.make(env_id)
# self.env.seed(config['general']['random_seed'])
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)
self.valid_env = gym.make(valid_env_id)
self.valid_env.seed(config['general']['random_seed'])
self.teacher_agent = get_agent(config, self.env)
print('Loading teacher from : ', teacher_path)
self.teacher_agent.model.load_state_dict(torch.load(teacher_path))
# import time; time.sleep(5)
self.hidden_size = config['model']['lstm_dqn']['action_scorer_hidden_dim']
self.hash_features = {}
def train(config, prune=False, embed='cnet'):
# 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'])
print("##" * 30)
if prune:
print('Using state pruning ...')
else:
print('Not using state pruning ...')
print("##" * 30)
# 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'])
# valid_env.reset()
# 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'])
# test_env_list[i].reset()
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.
use_cuda = config['general']['use_cuda']
revisit_counting = config['general']['revisit_counting']
replay_batch_size = config['general']['replay_batch_size']
history_size = config['general']['history_size']
update_from = config['general']['update_from']
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
if config['general']['exp_act']:
print('##' * 30)
print('Using expanded verb list')
verb_list = read_file("data/vocabs/trial_run_custom_tw/verb_vocab.txt")
object_name_list = read_file("data/vocabs/common_nouns.txt")
else:
#"This option only works for coin collector"
verb_list = ["go", "take", "unlock", "lock", "drop", "look", "insert", "open", "inventory", "close"]
object_name_list = ["east", "west", "north", "south", "coin", "apple", "carrot", "textbook", "passkey",
"keycard"]
# Add missing words in word2id
for w in verb_list:
if w not in word2id.keys():
word2id[w] = len(word2id)
word_vocab += [w, ]
for w in object_name_list:
if w not in word2id.keys():
word2id[w] = len(word2id)
word_vocab += [w, ]
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]
# teacher_path = config['general']['teacher_model_path']
# teacher_agent = Agent(config, word_vocab, verb_map, noun_map,
# att=config['general']['use_attention'],
# bootstrap=False,
# replay_memory_capacity=replay_memory_capacity,
# replay_memory_priority_fraction=replay_memory_priority_fraction)
# teacher_agent.model.load_state_dict(torch.load(teacher_path))
# teacher_agent.model.eval()
student_agent = Agent(config, word_vocab, verb_map, noun_map,
att=config['general']['use_attention'],
bootstrap=config['general']['student'],
replay_memory_capacity=replay_memory_capacity,
replay_memory_priority_fraction=replay_memory_priority_fraction,
embed=embed)
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, student_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']
model_checkpoint_path = config['training']['scheduling']['model_checkpoint_path']
epsilon = epsilon_anneal_from
revisit_counting_lambda = revisit_counting_lambda_anneal_from
#######################################################################
##### Load the teacher data #####
#######################################################################
prefix_name = get_prefix(args)
filename = './data/teacher_data/{}.npz'.format(prefix_name)
teacher_dict = np.load(filename, allow_pickle=True)
# import ipdb; ipdb.set_trace()
global_action_set = set()
print("##" * 30)
print("Training for {} epochs".format(config['training']['scheduling']['epoch']))
print("##" * 30)
import time
t0 = time.time()
for epoch in range(config['training']['scheduling']['epoch']):
student_agent.model.train()
obs, infos = env.reset()
student_agent.reset(infos)
# this the string identifier for leading the episodic action distribution
id_string = student_agent.get_observation_strings(infos)
cont_flag=False
for id_ in id_string:
if id_ not in teacher_dict.keys():
cont_flag=True
if cont_flag:
print('Skipping this epoch/.....')
continue
# Episodic action list
action_dist = [teacher_dict[id_string[k]][-1] for k in range(len(id_string))]
action_dist = [[x for x in item.keys()] for item in action_dist]
for item in action_dist:
global_action_set.update(item)
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 = []
curr_observation_strings = student_agent.get_observation_strings(infos)
if revisit_counting:
student_agent.reset_binarized_counter(batch_size)
revisit_counting_rewards = student_agent.get_binarized_count(curr_observation_strings)
current_game_step = 0
prev_actions = ["" for _ in range(batch_size)] if provide_prev_action else None
input_description, description_id_list, student_desc, _ =\
student_agent.get_game_step_info(obs, infos, prev_actions, prune=prune,
teacher_actions=action_dist, ret_desc=True,)
curr_ras_hidden, curr_ras_cell = None, None # ras: recurrent action scorer
memory_cache = [[] for _ in range(batch_size)]
solved = [0 for _ in range(batch_size)]
while not all(dones):
student_agent.model.train()
v_idx, n_idx, chosen_strings, curr_ras_hidden, curr_ras_cell = \
student_agent.generate_one_command(input_description, curr_ras_hidden,
curr_ras_cell, epsilon=0.0,
return_att=args.use_attention)
obs, rewards, dones, infos = env.step(chosen_strings)
curr_observation_strings = student_agent.get_observation_strings(infos)
# print(chosen_strings)
if provide_prev_action:
prev_actions = chosen_strings
# counting
if revisit_counting:
revisit_counting_rewards = student_agent.get_binarized_count(curr_observation_strings, update=True)
else:
revisit_counting_rewards = [0.0 for b in range(batch_size)]
student_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
student_agent.rewards.append(rewards)
student_agent.dones.append(dones)
student_agent.intermediate_rewards.append([info["intermediate_reward"] for info in infos])
# computer rewards, and push into replay memory
rewards_np, rewards_pt, mask_np,\
mask_pt, memory_mask = student_agent.compute_reward(revisit_counting_lambda=revisit_counting_lambda,
revisit_counting=revisit_counting)
###############################
##### Pruned state desc #####
###############################
curr_description_id_list = description_id_list
input_description, description_id_list, student_desc, _ =\
student_agent.get_game_step_info(obs, infos, prev_actions, prune=prune,
teacher_actions=action_dist, ret_desc=True,)
for b in range(batch_size):
if memory_mask[b] == 0:
continue
if dones[b] == 1 and rewards[b] == 0:
# last possible step
is_final = True
else:
is_final = mask_np[b] == 0
if rewards[b] > 0.0:
solved[b] = 1
# replay memory
memory_cache[b].append(
(curr_description_id_list[b], v_idx[b], n_idx[b], rewards_pt[b], mask_pt[b], dones[b],
is_final, curr_observation_strings[b]))
if current_game_step > 0 and current_game_step % config["general"]["update_per_k_game_steps"] == 0:
policy_loss = student_agent.update(replay_batch_size, history_size, update_from, 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_(student_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
for i, mc in enumerate(memory_cache):
for item in mc:
if replay_memory_priority_fraction == 0.0:
# vanilla replay memory
student_agent.replay_memory.push(*item)
else:
# prioritized replay memory
student_agent.replay_memory.push(solved[i], *item)
student_agent.finish()
avg_loss_in_this_game = np.mean(avg_loss_in_this_game)
reward_avg.add(student_agent.final_rewards.mean())
step_avg.add(student_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', student_agent.final_rewards.mean(), epoch + 1)
summary.add_scalar('curr_interm_reward', student_agent.final_intermediate_rewards.mean(), epoch + 1)
summary.add_scalar('curr_counting_reward', student_agent.final_counting_rewards.mean(), epoch + 1)
summary.add_scalar('avg_step', step_avg.value, epoch + 1)
summary.add_scalar('curr_step', student_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)
t1 = time.time()
summary.add_scalar('time', t1 - t0, 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), student_agent.final_rewards.mean(), student_agent.final_intermediate_rewards.mean(), student_agent.final_counting_rewards.mean(),
np.mean(step_avg.value), student_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:
torch.save(student_agent.model.state_dict(), model_checkpoint_path.replace('.pt', '_train.pt'))
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) % config["training"]["scheduling"]["logging_frequency"] == 0:
valid_R, valid_IR, valid_S = test(config, valid_env, student_agent, test_batch_size, word2id, prune=prune,
teacher_actions=[list(global_action_set)]*test_batch_size)
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
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(student_agent.model.state_dict(), model_checkpoint_path.replace('.pt', '_best.pt'))
print("========= saved checkpoint =========")
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)
def load_valid_env(self, valid_env_name):
test_batch_size = 1
valid_env_id = gym_textworld.make_batch(env_id=valid_env_name, batch_size=test_batch_size, parallel=True)
self.valid_env = gym.make(valid_env_id)
self.valid_env.seed(config['general']['random_seed'])
print('Loaded env name: ', valid_env_name)
