openrl.envs.vec_env package

Subpackages

• openrl.envs.vec_env.utils package
  • Submodules
  • openrl.envs.vec_env.utils.numpy_utils module
  • openrl.envs.vec_env.utils.share_memory module
  • openrl.envs.vec_env.utils.util module
  • Module contents
• openrl.envs.vec_env.vec_info package
  • Submodules
  • openrl.envs.vec_env.vec_info.base_vec_info module
  • openrl.envs.vec_env.vec_info.episode_rewards_info module
  • openrl.envs.vec_env.vec_info.nlp_vec_info module
  • openrl.envs.vec_env.vec_info.simple_vec_info module
  • Module contents
• openrl.envs.vec_env.wrappers package
  • Submodules
  • openrl.envs.vec_env.wrappers.base_wrapper module
  • openrl.envs.vec_env.wrappers.gen_data module
  • openrl.envs.vec_env.wrappers.reward_wrapper module
  • openrl.envs.vec_env.wrappers.vec_monitor_wrapper module
  • openrl.envs.vec_env.wrappers.zero_reward_wrapper module
  • Module contents

Submodules

openrl.envs.vec_env.async_venv module

An async vector environment.

enum openrl.envs.vec_env.async_venv.AsyncState(value)

基类：enum.Enum

Valid values are as follows:

DEFAULT = <AsyncState.DEFAULT: 'default'>

WAITING_RESET = <AsyncState.WAITING_RESET: 'reset'>

WAITING_STEP = <AsyncState.WAITING_STEP: 'step'>

WAITING_CALL = <AsyncState.WAITING_CALL: 'call'>

class openrl.envs.vec_env.async_venv.AsyncVectorEnv(env_fns: Sequence[Callable[[], gymnasium.core.Env]], observation_space: Optional[gymnasium.spaces.space.Space] = None, action_space: Optional[gymnasium.spaces.space.Space] = None, shared_memory: bool = False, copy: bool = True, context: Optional[str] = None, daemon: bool = True, worker: Optional[Callable] = None, render_mode: Optional[str] = None, auto_reset: bool = True)

基类：openrl.envs.vec_env.base_venv.BaseVecEnv

Vectorized environment that runs multiple environments in parallel.

It uses multiprocessing processes, and pipes for communication.

call_fetch(timeout: Optional[Union[int, float]] = None) → list

Calls all parent pipes and waits for the results.

Args:

timeout: Number of seconds before the call to call_fetch times out.

If None (default), the call to call_fetch never times out.

Returns:

List of the results of the individual calls to the method or property for each environment.

Raises:

NoAsyncCallError: Calling call_fetch without any prior call to call_send. TimeoutError: The call to call_fetch has timed out after timeout second(s).

call_send(name: str, *args, **kwargs)

Calls the method with name asynchronously and apply args and kwargs to the method.

Args:

name: Name of the method or property to call. *args: Arguments to apply to the method call. **kwargs: Keyword arguments to apply to the method call.

Raises:

ClosedEnvironmentError: If the environment was closed (if close() was previously called). AlreadyPendingCallError: Calling call_send while waiting for a pending call to complete

close_extras(timeout: Optional[Union[int, float]] = None, terminate: bool = False)

Close the environments & clean up the extra resources (processes and pipes).

Args:

timeout: Number of seconds before the call to close() times out. If None,

the call to close() never times out. If the call to close() times out, then all processes are terminated.

terminate: If True, then the close() operation is forced and all processes are terminated.

Raises:

TimeoutError: If close() timed out.

property env_name

exec_func_fetch(timeout: Optional[Union[int, float]] = None) → list

Calls all parent pipes and waits for the results.

Args:

timeout: Number of seconds before the call to exec_func_fetch times out.

If None (default), the call to exec_func_fetch never times out.

Returns:

List of the results of the individual calls to the method or property for each environment.

Raises:

NoAsyncCallError: Calling call_fetch without any prior call to call_send. TimeoutError: The call to call_fetch has timed out after timeout second(s).

exec_func_send(func: Callable, indices, *args, **kwargs)

Calls the method with name asynchronously and apply args and kwargs to the method.

Args:

func: a function. indices: Indices of the environments to call the method on. *args: Arguments to apply to the method call. **kwargs: Keyword arguments to apply to the method call.

Raises:

ClosedEnvironmentError: If the environment was closed (if close() was previously called). AlreadyPendingCallError: Calling call_send while waiting for a pending call to complete

get_attr(name: str)

Get a property from each parallel environment.

Args:

name (str): Name of the property to be get from each individual environment.

Returns:

The property with name

reset_fetch(timeout: Optional[Union[int, float]] = None) → Union[gymnasium.core.ObsType, Tuple[gymnasium.core.ObsType, dict]]

Waits for the calls triggered by reset_send() to finish and returns the results.

Args:

timeout: Number of seconds before the call to reset_fetch times out. If None, the call to reset_fetch never times out. seed: ignored options: ignored

Returns:

A tuple of batched observations and list of dictionaries

Raises:

ClosedEnvironmentError: If the environment was closed (if close() was previously called). NoAsyncCallError: If reset_fetch() was called without any prior call to reset_send(). TimeoutError: If reset_fetch() timed out.

reset_send(seed: Optional[Union[int, List[int]]] = None, options: Optional[dict] = None)

Send calls to the reset methods of the sub-environments.

To get the results of these calls, you may invoke reset_fetch().

Args:

seed: List of seeds for each environment options: The reset option

Raises:

ClosedEnvironmentError: If the environment was closed (if close() was previously called). AlreadyPendingCallError: If the environment is already waiting for a pending call to another

method (e.g. step_send()). This can be caused by two consecutive calls to reset_send(), with no call to reset_fetch() in between.

set_attr(name: str, values: Union[List[Any], Tuple[Any], object])

Sets an attribute of the sub-environments.

Args:

name: Name of the property to be set in each individual environment. values: Values of the property to be set to. If values is a list or

tuple, then it corresponds to the values for each individual environment, otherwise a single value is set for all environments.

Raises:

ValueError: Values must be a list or tuple with length equal to the number of environments. AlreadyPendingCallError: Calling set_attr while waiting for a pending call to complete.

step_fetch(timeout: Optional[Union[int, float]] = None) → Union[Tuple[Any, numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]], List[Dict[str, Any]]], Tuple[Any, numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]], List[Dict[str, Any]]]]

Wait for the calls to step in each sub-environment to finish.

Args:

timeout: Number of seconds before the call to step_fetch() times out. If None, the call to step_fetch() never times out.

Returns:

The batched environment step information, (obs, reward, terminated, truncated, info)

Raises:

ClosedEnvironmentError: If the environment was closed (if close() was previously called). NoAsyncCallError: If step_fetch() was called without any prior call to step_send(). TimeoutError: If step_fetch() timed out.

step_send(actions: numpy.ndarray)

Send the calls to step to each sub-environment.

Args:

actions: Batch of actions. element of action_space

Raises:

ClosedEnvironmentError: If the environment was closed (if close() was previously called). AlreadyPendingCallError: If the environment is already waiting for a pending call to another

method (e.g. reset_send()). This can be caused by two consecutive calls to step_send(), with no call to step_fetch() in between.

openrl.envs.vec_env.base_venv module

class openrl.envs.vec_env.base_venv.BaseVecEnv(parallel_env_num: int, observation_space: gymnasium.spaces.space.Space, action_space: gymnasium.spaces.space.Space, render_mode: Optional[str] = None, auto_reset: bool = True)

基类：abc.ABC

An abstract vectorized environment.

参数

• parallel_env_num -- Number of environments

• observation_space -- Observation space

• action_space -- Action space

action_space: gymnasium.spaces.space.Space

property agent_num

call(name: str, *args, **kwargs) → List[Any]

Call a method, or get a property, from each parallel environment.

Args:

name (str): Name of the method or property to call. *args: Arguments to apply to the method call. **kwargs: Keyword arguments to apply to the method call.

Returns:

List of the results of the individual calls to the method or property for each environment.

call_fetch(**kwargs) → List[Any]

After calling a method in call_send(), this function collects the results.

call_send(name, *args, **kwargs)

Calls a method name for each parallel environment asynchronously.

close(**kwargs) → None

Clean up the environment's resources.

close_extras(**kwargs)

Clean up the extra resources e.g. beyond what's in this base class.

closed = False

env_is_wrapped(wrapper_class: Type[openrl.envs.wrappers.base_wrapper.BaseWrapper], indices: Union[None, int, Iterable[int]] = None) → List[bool]

Check if worker environments are wrapped with a given wrapper

abstract property env_name

exec_func(func: Callable, indices: Optional[List[int]] = None, *args, **kwargs) → List[Any]

Call a method, or get a property, from each parallel environment.

Args:

func : Name of the method to call. indices: Indices of the environments to call the method on. *args: Arguments to apply to the method call. **kwargs: Keyword arguments to apply to the method call.

Returns:

List of the results of the individual calls to the method or property for each environment.

exec_func_fetch(timeout: Optional[Union[int, float]] = None) → list

Calls all parent pipes and waits for the results.

Args:

timeout: Number of seconds before the call to step_fetch times out.

If None (default), the call to step_fetch never times out.

Returns:

List of the results of the individual calls to the method or property for each environment.

Raises:

NoAsyncCallError: Calling call_fetch without any prior call to call_send. TimeoutError: The call to call_fetch has timed out after timeout second(s).

exec_func_send(func: Callable, indices, *args, **kwargs)

Calls the method with name asynchronously and apply args and kwargs to the method.

Args:

func: a function. indices: Indices of the environments to call the method on. *args: Arguments to apply to the method call. **kwargs: Keyword arguments to apply to the method call.

Raises:

ClosedEnvironmentError: If the environment was closed (if close() was previously called). AlreadyPendingCallError: Calling call_send while waiting for a pending call to complete

get_attr(name: str)

Get a property from each parallel environment.

Args:

name (str): Name of the property to be get from each individual environment.

Returns:

The property with name

metadata = {'render.modes': ['human', 'rgb_array', 'group_human', 'group_rgb_array', 'single_human', 'single_rgb_array']}

observation_space: gymnasium.spaces.space.Space

parallel_env_num: int

random_action(infos: Optional[List[Dict[str, Any]]] = None)

Get a random action from the action space

render(mode: Optional[str] = None) → Optional[numpy.ndarray]

Gym environment rendering

参数

mode -- the rendering type

reset(*, seed: Optional[Union[int, List[int]]] = None, options: Optional[dict] = None)

Reset all the environments and return an array of observations, or a tuple of observation arrays.

If step_send is still doing work, that work will be cancelled and step_fetch() should not be called until step_send() is invoked again.

返回

observation

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

Environment seeds can be passed to this reset argument in the future. The old .seed() method is being deprecated. Sets the random seeds for all environments, based on a given seed. Each individual environment will still get its own seed, by incrementing the given seed.

参数

seed -- The random seed. May be None for completely random seeding.

返回

Returns a list containing the seeds for each individual env. Note that all list elements may be None, if the env does not return anything when being seeded.

set_attr(name: str, values: Union[list, tuple, object])

Set a property in each sub-environment.

Args:

name (str): Name of the property to be set in each individual environment. values (list, tuple, or object): Values of the property to be set to. If values is a list or

tuple, then it corresponds to the values for each individual environment, otherwise a single value is set for all environments.

step(actions)

Step the environments with the given action

参数

actions -- the action

返回

observation, reward, done, information

property unwrapped: openrl.envs.vec_env.base_venv.BaseVecEnv

vector_render()

openrl.envs.vec_env.sync_venv module

class openrl.envs.vec_env.sync_venv.SyncVectorEnv(env_fns: Iterable[Callable[[], gymnasium.core.Env]], observation_space: gymnasium.spaces.space.Space = None, action_space: gymnasium.spaces.space.Space = None, copy: bool = True, render_mode: Optional[str] = None, auto_reset: bool = True)

基类：openrl.envs.vec_env.base_venv.BaseVecEnv

Vectorized environment that serially runs multiple environments.

call(name, *args, **kwargs) → tuple

Calls the method with name and applies args and kwargs.

Args:

name: The method name *args: The method args **kwargs: The method kwargs

Returns:

Tuple of results

close_extras(**kwargs)

Close the environments.

property env_name

exec_func(func: Callable, indices: Optional[List[int]] = None, *args, **kwargs) → tuple

Calls the method with name and applies args and kwargs.

Args:

func: The method name *args: The method args **kwargs: The method kwargs

Returns:

Tuple of results

format_obs(observations: Iterable) → Union[tuple, dict, numpy.ndarray]

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

Sets the seed in all sub-environments.

Args:

seed: The seed

set_attr(name: str, values: Union[list, tuple, Any])

Sets an attribute of the sub-environments.

Args:

name: The property name to change values: Values of the property to be set to. If values is a list or

tuple, then it corresponds to the values for each individual environment, otherwise, a single value is set for all environments.

Raises:

ValueError: Values must be a list or tuple with length equal to the number of environments.

Module contents

class openrl.envs.vec_env.AsyncVectorEnv(env_fns: Sequence[Callable[[], gymnasium.core.Env]], observation_space: Optional[gymnasium.spaces.space.Space] = None, action_space: Optional[gymnasium.spaces.space.Space] = None, shared_memory: bool = False, copy: bool = True, context: Optional[str] = None, daemon: bool = True, worker: Optional[Callable] = None, render_mode: Optional[str] = None, auto_reset: bool = True)

基类：openrl.envs.vec_env.base_venv.BaseVecEnv

Vectorized environment that runs multiple environments in parallel.

It uses multiprocessing processes, and pipes for communication.

call_fetch(timeout: Optional[Union[int, float]] = None) → list

Calls all parent pipes and waits for the results.

Args:

timeout: Number of seconds before the call to call_fetch times out.

If None (default), the call to call_fetch never times out.

Returns:

List of the results of the individual calls to the method or property for each environment.

Raises:

NoAsyncCallError: Calling call_fetch without any prior call to call_send. TimeoutError: The call to call_fetch has timed out after timeout second(s).

call_send(name: str, *args, **kwargs)

Calls the method with name asynchronously and apply args and kwargs to the method.

Args:

name: Name of the method or property to call. *args: Arguments to apply to the method call. **kwargs: Keyword arguments to apply to the method call.

Raises:

ClosedEnvironmentError: If the environment was closed (if close() was previously called). AlreadyPendingCallError: Calling call_send while waiting for a pending call to complete

close_extras(timeout: Optional[Union[int, float]] = None, terminate: bool = False)

Close the environments & clean up the extra resources (processes and pipes).

Args:

timeout: Number of seconds before the call to close() times out. If None,

the call to close() never times out. If the call to close() times out, then all processes are terminated.

terminate: If True, then the close() operation is forced and all processes are terminated.

Raises:

TimeoutError: If close() timed out.

property env_name

exec_func_fetch(timeout: Optional[Union[int, float]] = None) → list

Calls all parent pipes and waits for the results.

Args:

timeout: Number of seconds before the call to exec_func_fetch times out.

If None (default), the call to exec_func_fetch never times out.

Returns:

List of the results of the individual calls to the method or property for each environment.

Raises:

NoAsyncCallError: Calling call_fetch without any prior call to call_send. TimeoutError: The call to call_fetch has timed out after timeout second(s).

exec_func_send(func: Callable, indices, *args, **kwargs)

Calls the method with name asynchronously and apply args and kwargs to the method.

Args:

func: a function. indices: Indices of the environments to call the method on. *args: Arguments to apply to the method call. **kwargs: Keyword arguments to apply to the method call.

Raises:

ClosedEnvironmentError: If the environment was closed (if close() was previously called). AlreadyPendingCallError: Calling call_send while waiting for a pending call to complete

get_attr(name: str)

Get a property from each parallel environment.

Args:

name (str): Name of the property to be get from each individual environment.

Returns:

The property with name

reset_fetch(timeout: Optional[Union[int, float]] = None) → Union[gymnasium.core.ObsType, Tuple[gymnasium.core.ObsType, dict]]

Waits for the calls triggered by reset_send() to finish and returns the results.

Args:

timeout: Number of seconds before the call to reset_fetch times out. If None, the call to reset_fetch never times out. seed: ignored options: ignored

Returns:

A tuple of batched observations and list of dictionaries

Raises:

ClosedEnvironmentError: If the environment was closed (if close() was previously called). NoAsyncCallError: If reset_fetch() was called without any prior call to reset_send(). TimeoutError: If reset_fetch() timed out.

reset_send(seed: Optional[Union[int, List[int]]] = None, options: Optional[dict] = None)

Send calls to the reset methods of the sub-environments.

To get the results of these calls, you may invoke reset_fetch().

Args:

seed: List of seeds for each environment options: The reset option

Raises:

ClosedEnvironmentError: If the environment was closed (if close() was previously called). AlreadyPendingCallError: If the environment is already waiting for a pending call to another

method (e.g. step_send()). This can be caused by two consecutive calls to reset_send(), with no call to reset_fetch() in between.

set_attr(name: str, values: Union[List[Any], Tuple[Any], object])

Sets an attribute of the sub-environments.

Args:

name: Name of the property to be set in each individual environment. values: Values of the property to be set to. If values is a list or

tuple, then it corresponds to the values for each individual environment, otherwise a single value is set for all environments.

Raises:

ValueError: Values must be a list or tuple with length equal to the number of environments. AlreadyPendingCallError: Calling set_attr while waiting for a pending call to complete.

step_fetch(timeout: Optional[Union[int, float]] = None) → Union[Tuple[Any, numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]], List[Dict[str, Any]]], Tuple[Any, numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]], List[Dict[str, Any]]]]

Wait for the calls to step in each sub-environment to finish.

Args:

timeout: Number of seconds before the call to step_fetch() times out. If None, the call to step_fetch() never times out.

Returns:

The batched environment step information, (obs, reward, terminated, truncated, info)

Raises:

ClosedEnvironmentError: If the environment was closed (if close() was previously called). NoAsyncCallError: If step_fetch() was called without any prior call to step_send(). TimeoutError: If step_fetch() timed out.

step_send(actions: numpy.ndarray)

Send the calls to step to each sub-environment.

Args:

actions: Batch of actions. element of action_space

Raises:

ClosedEnvironmentError: If the environment was closed (if close() was previously called). AlreadyPendingCallError: If the environment is already waiting for a pending call to another

method (e.g. reset_send()). This can be caused by two consecutive calls to step_send(), with no call to step_fetch() in between.

class openrl.envs.vec_env.BaseVecEnv(parallel_env_num: int, observation_space: gymnasium.spaces.space.Space, action_space: gymnasium.spaces.space.Space, render_mode: Optional[str] = None, auto_reset: bool = True)

基类：abc.ABC

An abstract vectorized environment.

参数

• parallel_env_num -- Number of environments

• observation_space -- Observation space

• action_space -- Action space

action_space: gymnasium.spaces.space.Space

property agent_num

call(name: str, *args, **kwargs) → List[Any]

Call a method, or get a property, from each parallel environment.

Args:

name (str): Name of the method or property to call. *args: Arguments to apply to the method call. **kwargs: Keyword arguments to apply to the method call.

Returns:

List of the results of the individual calls to the method or property for each environment.

call_fetch(**kwargs) → List[Any]

After calling a method in call_send(), this function collects the results.

call_send(name, *args, **kwargs)

Calls a method name for each parallel environment asynchronously.

close(**kwargs) → None

Clean up the environment's resources.

close_extras(**kwargs)

Clean up the extra resources e.g. beyond what's in this base class.

closed = False

env_is_wrapped(wrapper_class: Type[openrl.envs.wrappers.base_wrapper.BaseWrapper], indices: Union[None, int, Iterable[int]] = None) → List[bool]

Check if worker environments are wrapped with a given wrapper

abstract property env_name

exec_func(func: Callable, indices: Optional[List[int]] = None, *args, **kwargs) → List[Any]

Call a method, or get a property, from each parallel environment.

Args:

func : Name of the method to call. indices: Indices of the environments to call the method on. *args: Arguments to apply to the method call. **kwargs: Keyword arguments to apply to the method call.

Returns:

List of the results of the individual calls to the method or property for each environment.

exec_func_fetch(timeout: Optional[Union[int, float]] = None) → list

Calls all parent pipes and waits for the results.

Args:

timeout: Number of seconds before the call to step_fetch times out.

If None (default), the call to step_fetch never times out.

Returns:

List of the results of the individual calls to the method or property for each environment.

Raises:

NoAsyncCallError: Calling call_fetch without any prior call to call_send. TimeoutError: The call to call_fetch has timed out after timeout second(s).

exec_func_send(func: Callable, indices, *args, **kwargs)

Calls the method with name asynchronously and apply args and kwargs to the method.

Args:

func: a function. indices: Indices of the environments to call the method on. *args: Arguments to apply to the method call. **kwargs: Keyword arguments to apply to the method call.

Raises:

ClosedEnvironmentError: If the environment was closed (if close() was previously called). AlreadyPendingCallError: Calling call_send while waiting for a pending call to complete

get_attr(name: str)

Get a property from each parallel environment.

Args:

name (str): Name of the property to be get from each individual environment.

Returns:

The property with name

metadata = {'render.modes': ['human', 'rgb_array', 'group_human', 'group_rgb_array', 'single_human', 'single_rgb_array']}

observation_space: gymnasium.spaces.space.Space

parallel_env_num: int

random_action(infos: Optional[List[Dict[str, Any]]] = None)

Get a random action from the action space

render(mode: Optional[str] = None) → Optional[numpy.ndarray]

Gym environment rendering

参数

mode -- the rendering type

reset(*, seed: Optional[Union[int, List[int]]] = None, options: Optional[dict] = None)

Reset all the environments and return an array of observations, or a tuple of observation arrays.

If step_send is still doing work, that work will be cancelled and step_fetch() should not be called until step_send() is invoked again.

返回

observation

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

Environment seeds can be passed to this reset argument in the future. The old .seed() method is being deprecated. Sets the random seeds for all environments, based on a given seed. Each individual environment will still get its own seed, by incrementing the given seed.

参数

seed -- The random seed. May be None for completely random seeding.

返回

Returns a list containing the seeds for each individual env. Note that all list elements may be None, if the env does not return anything when being seeded.

set_attr(name: str, values: Union[list, tuple, object])

Set a property in each sub-environment.

Args:

name (str): Name of the property to be set in each individual environment. values (list, tuple, or object): Values of the property to be set to. If values is a list or

tuple, then it corresponds to the values for each individual environment, otherwise a single value is set for all environments.

step(actions)

Step the environments with the given action

参数

actions -- the action

返回

observation, reward, done, information

property unwrapped: openrl.envs.vec_env.base_venv.BaseVecEnv

vector_render()

class openrl.envs.vec_env.RewardWrapper(env: openrl.envs.vec_env.base_venv.BaseVecEnv, reward_class: openrl.rewards.base_reward.BaseReward)

基类：openrl.envs.vec_env.wrappers.base_wrapper.VecEnvWrapper

batch_rewards(buffer)

step(action: gymnasium.core.ActType, extra_data: Optional[Dict[str, Any]]) → Union[Any, numpy.ndarray, List[Dict[str, Any]]]

Step all environments.

class openrl.envs.vec_env.SyncVectorEnv(env_fns: Iterable[Callable[[], gymnasium.core.Env]], observation_space: gymnasium.spaces.space.Space = None, action_space: gymnasium.spaces.space.Space = None, copy: bool = True, render_mode: Optional[str] = None, auto_reset: bool = True)

基类：openrl.envs.vec_env.base_venv.BaseVecEnv

Vectorized environment that serially runs multiple environments.

call(name, *args, **kwargs) → tuple

Calls the method with name and applies args and kwargs.

Args:

name: The method name *args: The method args **kwargs: The method kwargs

Returns:

Tuple of results

close_extras(**kwargs)

Close the environments.

property env_name

exec_func(func: Callable, indices: Optional[List[int]] = None, *args, **kwargs) → tuple

Calls the method with name and applies args and kwargs.

Args:

func: The method name *args: The method args **kwargs: The method kwargs

Returns:

Tuple of results

format_obs(observations: Iterable) → Union[tuple, dict, numpy.ndarray]

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

Sets the seed in all sub-environments.

Args:

seed: The seed

set_attr(name: str, values: Union[list, tuple, Any])

Sets an attribute of the sub-environments.

Args:

name: The property name to change values: Values of the property to be set to. If values is a list or

tuple, then it corresponds to the values for each individual environment, otherwise, a single value is set for all environments.

Raises:

ValueError: Values must be a list or tuple with length equal to the number of environments.

class openrl.envs.vec_env.VecMonitorWrapper(vec_info: openrl.envs.vec_env.vec_info.base_vec_info.BaseVecInfo, env: openrl.envs.vec_env.base_venv.BaseVecEnv)

基类：openrl.envs.vec_env.wrappers.base_wrapper.VecEnvWrapper

statistics(buffer)

step(action: gymnasium.core.ActType, extra_data: Optional[Dict[str, Any]] = None)

Step all environments.

property use_monitor