tianshou.env

VectorEnv

BaseVectorEnv

class tianshou.env.BaseVectorEnv(env_fns: List[Callable[], gym.core.Env]], worker_fn: Callable[[Callable[], gym.core.Env]], tianshou.env.worker.base.EnvWorker], wait_num: Optional[int] = None, timeout: Optional[float] = None, norm_obs: bool = False, obs_rms: Optional[tianshou.utils.statistics.RunningMeanStd] = None, update_obs_rms: bool = True)[source]

Bases: gym.core.Env

Base class for vectorized environments wrapper.

Usage:

env_num = 8
envs = DummyVectorEnv([lambda: gym.make(task) for _ in range(env_num)])
assert len(envs) == env_num

It accepts a list of environment generators. In other words, an environment generator efn of a specific task means that efn() returns the environment of the given task, for example, gym.make(task).

All of the VectorEnv must inherit BaseVectorEnv. Here are some other usages:

envs.seed(2)  # which is equal to the next line
envs.seed([2, 3, 4, 5, 6, 7, 8, 9])  # set specific seed for each env
obs = envs.reset()  # reset all environments
obs = envs.reset([0, 5, 7])  # reset 3 specific environments
obs, rew, done, info = envs.step([1] * 8)  # step synchronously
envs.render()  # render all environments
envs.close()  # close all environments

Warning

If you use your own environment, please make sure the seed method is set up properly, e.g.,

def seed(self, seed):
    np.random.seed(seed)

Otherwise, the outputs of these envs may be the same with each other.

Parameters
  • env_fns – a list of callable envs, env_fns[i]() generates the i-th env.

  • worker_fn – a callable worker, worker_fn(env_fns[i]) generates a worker which contains the i-th env.

  • wait_num (int) – use in asynchronous simulation if the time cost of env.step varies with time and synchronously waiting for all environments to finish a step is time-wasting. In that case, we can return when wait_num environments finish a step and keep on simulation in these environments. If None, asynchronous simulation is disabled; else, 1 <= wait_num <= env_num.

  • timeout (float) – use in asynchronous simulation same as above, in each vectorized step it only deal with those environments spending time within timeout seconds.

  • norm_obs (bool) – Whether to track mean/std of data and normalize observation on return. For now, observation normalization only support observation of type np.ndarray.

  • obs_rms – class to track mean&std of observation. If not given, it will initialize a new one. Usually in envs that is used to evaluate algorithm, obs_rms should be passed in. Default to None.

  • update_obs_rms (bool) – Whether to update obs_rms. Default to True.

__len__()int[source]

Return len(self), which is the number of environments.

get_env_attr(key: str, id: Optional[Union[int, List[int], numpy.ndarray]] = None)List[Any][source]

Get an attribute from the underlying environments.

If id is an int, retrieve the attribute denoted by key from the environment underlying the worker at index id. The result is returned as a list with one element. Otherwise, retrieve the attribute for all workers at indices id and return a list that is ordered correspondingly to id.

Parameters
  • key (str) – The key of the desired attribute.

  • id – Indice(s) of the desired worker(s). Default to None for all env_id.

Return list

The list of environment attributes.

set_env_attr(key: str, value: Any, id: Optional[Union[int, List[int], numpy.ndarray]] = None)None[source]

Set an attribute in the underlying environments.

If id is an int, set the attribute denoted by key from the environment underlying the worker at index id to value. Otherwise, set the attribute for all workers at indices id.

Parameters
  • key (str) – The key of the desired attribute.

  • value (Any) – The new value of the attribute.

  • id – Indice(s) of the desired worker(s). Default to None for all env_id.

reset(id: Optional[Union[int, List[int], numpy.ndarray]] = None)numpy.ndarray[source]

Reset the state of some envs and return initial observations.

If id is None, reset the state of all the environments and return initial observations, otherwise reset the specific environments with the given id, either an int or a list.

step(action: numpy.ndarray, id: Optional[Union[int, List[int], numpy.ndarray]] = None)Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray][source]

Run one timestep of some environments’ dynamics.

If id is None, run one timestep of all the environments’ dynamics; otherwise run one timestep for some environments with given id, either an int or a list. When the end of episode is reached, you are responsible for calling reset(id) to reset this environment’s state.

Accept a batch of action and return a tuple (batch_obs, batch_rew, batch_done, batch_info) in numpy format.

Parameters

action (numpy.ndarray) – a batch of action provided by the agent.

Returns

A tuple including four items:

  • obs a numpy.ndarray, the agent’s observation of current environments

  • rew a numpy.ndarray, the amount of rewards returned after previous actions

  • done a numpy.ndarray, whether these episodes have ended, in which case further step() calls will return undefined results

  • info a numpy.ndarray, contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)

For the async simulation:

Provide the given action to the environments. The action sequence should correspond to the id argument, and the id argument should be a subset of the env_id in the last returned info (initially they are env_ids of all the environments). If action is None, fetch unfinished step() calls instead.

seed(seed: Optional[Union[int, List[int]]] = None)List[Optional[List[int]]][source]

Set the seed for all environments.

Accept None, an int (which will extend i to [i, i + 1, i + 2, ...]) or a list.

Returns

The list of seeds used in this env’s random number generators. The first value in the list should be the “main” seed, or the value which a reproducer pass to “seed”.

render(**kwargs: Any)List[Any][source]

Render all of the environments.

close()None[source]

Close all of the environments.

This function will be called only once (if not, it will be called during garbage collected). This way, close of all workers can be assured.

normalize_obs(obs: numpy.ndarray)numpy.ndarray[source]

Normalize observations by statistics in obs_rms.

DummyVectorEnv

class tianshou.env.DummyVectorEnv(env_fns: List[Callable[], gym.core.Env]], **kwargs: Any)[source]

Bases: tianshou.env.venvs.BaseVectorEnv

Dummy vectorized environment wrapper, implemented in for-loop.

See also

Please refer to BaseVectorEnv for other APIs’ usage.

SubprocVectorEnv

class tianshou.env.SubprocVectorEnv(env_fns: List[Callable[], gym.core.Env]], **kwargs: Any)[source]

Bases: tianshou.env.venvs.BaseVectorEnv

Vectorized environment wrapper based on subprocess.

See also

Please refer to BaseVectorEnv for other APIs’ usage.

ShmemVectorEnv

class tianshou.env.ShmemVectorEnv(env_fns: List[Callable[], gym.core.Env]], **kwargs: Any)[source]

Bases: tianshou.env.venvs.BaseVectorEnv

Optimized SubprocVectorEnv with shared buffers to exchange observations.

ShmemVectorEnv has exactly the same API as SubprocVectorEnv.

See also

Please refer to BaseVectorEnv for other APIs’ usage.

RayVectorEnv

class tianshou.env.RayVectorEnv(env_fns: List[Callable[], gym.core.Env]], **kwargs: Any)[source]

Bases: tianshou.env.venvs.BaseVectorEnv

Vectorized environment wrapper based on ray.

This is a choice to run distributed environments in a cluster.

See also

Please refer to BaseVectorEnv for other APIs’ usage.

Worker

EnvWorker

class tianshou.env.worker.EnvWorker(env_fn: Callable[], gym.core.Env])[source]

Bases: abc.ABC

An abstract worker for an environment.

abstract get_env_attr(key: str)Any[source]
abstract set_env_attr(key: str, value: Any)None[source]
abstract reset()Any[source]
abstract send_action(action: numpy.ndarray)None[source]
get_result()Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray][source]
step(action: numpy.ndarray)Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray][source]

Perform one timestep of the environment’s dynamic.

“send_action” and “get_result” are coupled in sync simulation, so typically users only call “step” function. But they can be called separately in async simulation, i.e. someone calls “send_action” first, and calls “get_result” later.

static wait(workers: List[tianshou.env.worker.base.EnvWorker], wait_num: int, timeout: Optional[float] = None)List[tianshou.env.worker.base.EnvWorker][source]

Given a list of workers, return those ready ones.

seed(seed: Optional[int] = None)Optional[List[int]][source]
abstract render(**kwargs: Any)Any[source]

Render the environment.

abstract close_env()None[source]
close()None[source]

DummyEnvWorker

class tianshou.env.worker.DummyEnvWorker(env_fn: Callable[], gym.core.Env])[source]

Bases: tianshou.env.worker.base.EnvWorker

Dummy worker used in sequential vector environments.

get_env_attr(key: str)Any[source]
set_env_attr(key: str, value: Any)None[source]
reset()Any[source]
static wait(workers: List[tianshou.env.worker.dummy.DummyEnvWorker], wait_num: int, timeout: Optional[float] = None)List[tianshou.env.worker.dummy.DummyEnvWorker][source]

Given a list of workers, return those ready ones.

send_action(action: numpy.ndarray)None[source]
seed(seed: Optional[int] = None)List[int][source]
render(**kwargs: Any)Any[source]

Render the environment.

close_env()None[source]

SubprocEnvWorker

class tianshou.env.worker.SubprocEnvWorker(env_fn: Callable[], gym.core.Env], share_memory: bool = False)[source]

Bases: tianshou.env.worker.base.EnvWorker

Subprocess worker used in SubprocVectorEnv and ShmemVectorEnv.

get_env_attr(key: str)Any[source]
set_env_attr(key: str, value: Any)None[source]
reset()Any[source]
static wait(workers: List[tianshou.env.worker.subproc.SubprocEnvWorker], wait_num: int, timeout: Optional[float] = None)List[tianshou.env.worker.subproc.SubprocEnvWorker][source]

Given a list of workers, return those ready ones.

send_action(action: numpy.ndarray)None[source]
get_result()Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray][source]
seed(seed: Optional[int] = None)Optional[List[int]][source]
render(**kwargs: Any)Any[source]

Render the environment.

close_env()None[source]

RayEnvWorker

class tianshou.env.worker.RayEnvWorker(env_fn: Callable[], gym.core.Env])[source]

Bases: tianshou.env.worker.base.EnvWorker

Ray worker used in RayVectorEnv.

get_env_attr(key: str)Any[source]
set_env_attr(key: str, value: Any)None[source]
reset()Any[source]
static wait(workers: List[tianshou.env.worker.ray.RayEnvWorker], wait_num: int, timeout: Optional[float] = None)List[tianshou.env.worker.ray.RayEnvWorker][source]

Given a list of workers, return those ready ones.

send_action(action: numpy.ndarray)None[source]
get_result()Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray][source]
seed(seed: Optional[int] = None)List[int][source]
render(**kwargs: Any)Any[source]

Render the environment.

close_env()None[source]