Envs

class textworld.gym.envs.textworld.TextworldGymEnv(gamefiles, request_infos=None, max_episode_steps=None, **kwargs)

Bases: TextworldBatchGymEnv

Environment for playing text-based games.

Parameters:

• gamefiles (List[str]) – Paths of every game composing the pool (*.ulx|*.z[1-8]).

• request_infos (Optional[EnvInfos]) –

  For customizing the information returned by this environment (see textworld.EnvInfos for the list of available information).

  Warning

  Only supported for TextWorld games (i.e., that have a corresponding *.json file).

• max_episode_steps (Optional[int]) – Number of steps allocated to play each game. Once exhausted, the game is done.

reset()

Resets the text-based environment.

Resetting this environment means starting the next game in the pool.

Return type:

Tuple[str, Dict[str, Any]]

Returns:

A tuple (observation, info) where

• observation: text observed in the initial state;

• infos: additional information as requested.

step(command)

Runs a command in the text-based environment.

Parameters:

command – Text command to send to the game interpreter.

Return type:

Tuple[str, Dict[str, Any]]

Returns:

A tuple (observation, score, done, info) where

• observation: text observed in the new state;

• score: total number of points accumulated so far;

• done: whether the game is finished or not;

• infos: additional information as requested.

class textworld.gym.envs.textworld_batch.TextworldBatchGymEnv(gamefiles, request_infos=None, batch_size=1, asynchronous=True, auto_reset=False, max_episode_steps=None, wrappers=[])

Bases: object

Environment for playing text-based games in batch.

Parameters:

• gamefiles (List[str]) – Paths of every game composing the pool (*.ulx|*.z[1-8]|*.json).

• request_infos (Optional[EnvInfos]) –

  For customizing the information returned by this environment (see textworld.EnvInfos for the list of available information).

  Warning

  Only supported for TextWorld games (i.e., that have a corresponding *.json file).

• batch_size (int) –

  If provided, it indicates the number of games to play at the same time. By default, a single game is played at once.

  Warning

  When batch_size is provided (even for batch_size=1), env.step expects a list of commands as input and outputs a list of states. env.reset also outputs a list of states.

• asynchronous (bool) – If True, wraps the environments in an AsyncBatchEnv (which uses multiprocessing to run the environments in parallel). If False, wraps the environments in a SyncBatchEnv. Default: True.

• auto_reset (bool) – If True, each game independently resets once it is done (i.e., reset happens on the next env.step call). Otherwise, once a game is done, subsequent calls to env.step won’t have any effects.

• max_episode_steps (Optional[int]) – Number of steps allocated to play each game. Once exhausted, the game is done.

close()

Close this environment.

Return type:

None

render(mode='human')

Renders the current state of each environment in the batch.

Each rendering is composed of the previous text command (if there’s one) and the text describing the current observation.

Parameters:

mode (str) –

Controls where and how the text is rendered. Supported modes are:

• human: Display text to the current display or terminal and return nothing.

• ansi: Return a StringIO containing a terminal-style text representation. The text can include newlines and ANSI escape sequences (e.g. for colors).

• text: Return a string (str) containing the text without any ANSI escape sequences.

Return type:

Union[StringIO, str, None]

Returns:

Depending on the mode, this method returns either nothing, a string, or a StringIO object.

reset()

Resets the text-based environment.

Resetting this environment means starting the next game in the pool.

Return type:

Tuple[List[str], Dict[str, List[Any]]]

Returns:

A tuple (observations, infos) where

• observation: text observed in the initial state for each game in the batch;

• infos: additional information as requested for each game in the batch.

seed(seed=None)

Set the seed for this environment’s random generator(s).

This environment use a random generator to shuffle the order in which the games are played.

Parameters:

seed (Optional[int]) – Number that will be used to seed the random generators.

Return type:

List[int]

Returns:

All the seeds used to set this environment’s random generator(s).

skip(nb_games=1)

Skip games.

Parameters:

nb_games (int) – Number of games to skip.

Return type:

None

step(commands)

Runs a command in each text-based environment of the batch.

Parameters:

commands – Text command to send to the game interpreter.

Return type:

Tuple[List[str], List[float], List[bool], Dict[str, List[Any]]]

Returns:

A tuple (observations, scores, dones, infos) where

• observations: text observed in the new state for each game in the batch;

• scores: total number of points accumulated so far for each game in the batch;

• dones: whether each game in the batch is finished or not;

• infos: additional information as requested for each game in the batch.

textworld.gym.envs.utils.shuffled_cycle(iterable, rng, nb_loops=-1)

Yield each element of iterable one by one, then shuffle the elements and start yielding from the start. Stop after nb_loops loops.

Parameters:

• iterable (Iterable[Any]) – Iterable containing the elements to yield.

• rng (RandomState) – Random generator used to shuffle the elements after each loop.

• nb_loops (int) – Number of times to go through all the elements. If set to -1, loop an infinite number of times.

Return type:

Iterable[Any]