def get_game_step_info(self, obs: List[str], infos: Dict[str, List[Any]]):
"""
Get all the available information, and concat them together to be tensor for
a neural model. we use post padding here, all information are tokenized here.
Arguments:
obs: Previous command's feedback for each game.
infos: Additional information for each game.
"""
inventory_token_list = [preproc(item, tokenizer=self.nlp) for item in infos["inventory"]]
inventory_id_list = [_words_to_ids(tokens, self.word2id) for tokens in inventory_token_list]
feedback_token_list = [preproc(item, str_type='feedback', tokenizer=self.nlp) for item in obs]
feedback_id_list = [_words_to_ids(tokens, self.word2id) for tokens in feedback_token_list]
quest_token_list = [preproc(item, tokenizer=self.nlp) for item in infos["extra.recipe"]]
quest_id_list = [_words_to_ids(tokens, self.word2id) for tokens in quest_token_list]
prev_action_token_list = [preproc(item, tokenizer=self.nlp) for item in self.prev_actions]
prev_action_id_list = [_words_to_ids(tokens, self.word2id) for tokens in prev_action_token_list]
description_token_list = [preproc(item, tokenizer=self.nlp) for item in infos["description"]]
for i, d in enumerate(description_token_list):
if len(d) == 0:
description_token_list[i] = ["end"] # if empty description, insert word "end"
description_id_list = [_words_to_ids(tokens, self.word2id) for tokens in description_token_list]
description_id_list = [_d + _i + _q + _f + _pa for (_d, _i, _q, _f, _pa) in zip(description_id_list, inventory_id_list, quest_id_list, feedback_id_list, prev_action_id_list)]
input_description = pad_sequences(description_id_list, maxlen=max_len(description_id_list)).astype('int32')
input_description = to_pt(input_description, self.use_cuda)
return input_description, description_id_list
def get_game_info_at_certain_step(self, obs, infos):
"""
Get all needed info from game engine for training.
Arguments:
obs: Previous command's feedback for each game.
infos: Additional information for each game.
"""
batch_size = len(obs)
feedback_strings = [preproc(item, tokenizer=self.nlp) for item in obs]
description_strings = [
preproc(item, tokenizer=self.nlp) for item in infos["description"]
]
observation_strings = [
d + " <|> " + fb if fb != d else d + " <|> hello"
for fb, d in zip(feedback_strings, description_strings)
]
inventory_strings = [
preproc(item, tokenizer=self.nlp) for item in infos["inventory"]
]
local_word_list = [
obs.split() + inv.split()
for obs, inv in zip(observation_strings, inventory_strings)
]
directions = ["east", "west", "north", "south"]
if self.question_type in ["location", "existence"]:
# agents observes the env, but do not change them
possible_verbs = [["go", "inventory", "wait", "open", "examine"]
for _ in range(batch_size)]
else:
possible_verbs = [
list(set(item) - set(["", "look"])) for item in infos["verbs"]
]
possible_adjs, possible_nouns = [], []
for i in range(batch_size):
object_nouns = [
item.split()[-1] for item in infos["object_nouns"][i]
]
object_adjs = [
w for item in infos["object_adjs"][i] for w in item.split()
]
possible_nouns.append(
list(set(object_nouns) & set(local_word_list[i]) - set([""])) +
directions)
possible_adjs.append(
list(set(object_adjs) & set(local_word_list[i]) - set([""])) +
["</s>"])
return observation_strings, [
possible_verbs, possible_adjs, possible_nouns
]
def collect_data_from_game(gamefile, seed, branching_depth):
tokenizer = spacy.load('en', disable=['ner', 'parser', 'tagger'])
rng = np.random.RandomState(seed)
# Ignore the following commands.
commands_to_ignore = ["look", "examine", "inventory"]
env_infos = textworld.EnvInfos(description=True,
location=True,
facts=True,
last_action=True,
admissible_commands=True,
game=True,
extras=["walkthrough"])
env = textworld.start(gamefile, env_infos)
env = textworld.envs.wrappers.Filter(env)
obs, infos = env.reset()
walkthrough = infos["extra.walkthrough"]
# Make sure we start with listing the inventory.
if walkthrough[0] != "inventory":
walkthrough = ["inventory"] + walkthrough
# Add 'restart' command as a way to indicate the beginning of the game.
walkthrough = ["restart"] + walkthrough
dataset = []
done = False
facts_seen = set()
for i, cmd in enumerate(walkthrough):
last_facts = facts_seen
if i > 0: # != "restart"
obs, _, done, infos = env.step(cmd)
facts_seen = process_facts(last_facts, infos["game"], infos["facts"],
infos["last_action"], cmd)
dataset += [{
"game":
os.path.basename(gamefile),
"step": (i, 0),
"observation":
preproc(obs, tokenizer=tokenizer),
"previous_action":
cmd.lower(),
"target_commands":
sorted(
gen_graph_commands(facts_seen - last_facts, cmd="add") +
gen_graph_commands(last_facts - facts_seen, cmd="delete")),
"previous_graph_seen":
sorted(serialize_facts(last_facts)),
"graph_seen":
sorted(serialize_facts(facts_seen)),
}]
if done:
break # Stop collecting data if game is done.
# Fork the current game & seen facts.
env_ = env.copy()
facts_seen_ = facts_seen
# Then, take N random actions.
for j in range(1, branching_depth + 1):
commands = [
c for c in infos["admissible_commands"]
if ((c == "examine cookbook"
or c.split()[0] not in commands_to_ignore) and
(i + 1) != len(walkthrough) and c != walkthrough[i + 1])
]
if len(commands) == 0:
break
cmd_ = rng.choice(commands)
obs, _, done, infos = env_.step(cmd_)
if done:
break # Stop collecting data if game is done.
last_facts_ = facts_seen_
facts_seen_ = process_facts(last_facts_, infos["game"],
infos["facts"], infos["last_action"],
cmd_)
dataset += [{
"game":
os.path.basename(gamefile),
"step": (i, j),
"observation":
preproc(obs, tokenizer=tokenizer),
"previous_action":
cmd_.lower(),
"target_commands":
sorted(
gen_graph_commands(facts_seen_ - last_facts_, cmd="add") +
gen_graph_commands(last_facts_ -
facts_seen_, cmd="delete")),
"previous_graph_seen":
sorted(serialize_facts(last_facts_)),
"graph_seen":
sorted(serialize_facts(facts_seen_)),
}]
return gamefile, dataset
