RL [omni.isaac.gym]

Base Environment Wrapper

class VecEnvBase(headless: bool, sim_device: int = 0, enable_livestream: bool = False, enable_viewport: bool = False)

This class provides a base interface for connecting RL policies with task implementations. APIs provided in this interface follow the interface in gym.Env. This class also provides utilities for initializing simulation apps, creating the World, and registering a task.

action_space: gym.spaces.space.Space[gym.core.ActType]

close() → None

Closes simulation.

metadata: Dict[str, Any] = {'render_modes': []}

property np_random: numpy.random._generator.Generator

Returns the environment’s internal _np_random that if not set will initialise with a random seed.

property num_envs

Retrieves number of environments.

Returns

Number of environments.

Return type

num_envs(int)

observation_space: gym.spaces.space.Space[gym.core.ObsType]

render(mode='human') → None

Step the renderer.

Parameters

mode (str) – Select mode of rendering based on OpenAI environments.

render_mode: Optional[str] = None

reset()

Resets the task and updates observations.

reward_range = (-inf, inf)

seed(seed=- 1)

Sets a seed. Pass in -1 for a random seed.

Parameters

seed (int) – Seed to set. Defaults to -1.

Returns

Seed that was set.

Return type

seed (int)

set_task(task, backend='numpy', sim_params=None, init_sim=True) → None

Creates a World object and adds Task to World.

Initializes and registers task to the environment interface. Triggers task start-up.

Parameters

• task (RLTask) – The task to register to the env.

• backend (str) – Backend to use for task. Can be “numpy” or “torch”. Defaults to “numpy”.

• sim_params (dict) – Simulation parameters for physics settings. Defaults to None.

• init_sim (Optional[bool]) – Automatically starts simulation. Defaults to True.

spec: EnvSpec = None

step(actions)

Basic implementation for stepping simulation.

Can be overriden by inherited Env classes to satisfy requirements of specific RL libraries. This method passes actions to task for processing, steps simulation, and computes observations, rewards, and resets.

Parameters

actions (Union[numpy.ndarray, torch.Tensor]) – Actions buffer from policy.

Returns

Buffer of observation data. rewards(Union[numpy.ndarray, torch.Tensor]): Buffer of rewards data. dones(Union[numpy.ndarray, torch.Tensor]): Buffer of resets/dones data. info(dict): Dictionary of extras data.

Return type

observations(Union[numpy.ndarray, torch.Tensor])

property unwrapped: gym.core.Env

Returns the base non-wrapped environment.

Returns

The base non-wrapped gym.Env instance

Return type

Env

Multi-Threaded Environment Wrapper

exception TaskStopException

Exception class for signalling task termination.

args

with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

class TrainerMT

A base abstract trainer class for controlling starting and stopping of RL policy.

abstract run()

Runs RL loop in a new thread

abstract stop()

Stop RL thread

class VecEnvMT(headless: bool, sim_device: int = 0, enable_livestream: bool = False, enable_viewport: bool = False)

This class provides a base interface for connecting RL policies with task implementations in a multi-threaded fashion. RL policies using this class will run on a different thread than the thread simulation runs on. This can be useful for interacting with the UI before, during, and after running RL policies. Data sharing between threads happen through message passing on multi-threaded queues.

action_space: gym.spaces.space.Space[gym.core.ActType]

clear_queues()

Clears all queues.

close() → None

Closes simulation.

get_actions(block=True)

Retrieves actions from policy by waiting for actions to be sent to the queue from the RL thread.

Parameters

block (Optional[bool]) – Whether to block thread when waiting for data.

Returns

actions buffer retrieved from queue.

Return type

actions (Union[np.ndarray, torch.Tensor, None])

get_data(block=True)

Retrieves data from task by waiting for data dictionary to be sent to the queue from the simulation thread.

Parameters

block (Optional[bool]) – Whether to block thread when waiting for data.

Returns

data dictionary retrieved from queue.

Return type

actions (Union[np.ndarray, torch.Tensor, None])

initialize(action_queue, data_queue, timeout=30)

Initializes queues for sharing data across threads.

Parameters

• action_queue (queue.Queue) – Queue for passing actions from policy to task.

• data_queue (queue.Queue) – Queue for passing data from task to policy.

• timeout (Optional[int]) – Seconds to wait for data when queue is empty. An exception will be thrown when the timeout limit is reached. Defaults to 30 seconds.

metadata: Dict[str, Any] = {'render_modes': []}

property np_random: numpy.random._generator.Generator

Returns the environment’s internal _np_random that if not set will initialise with a random seed.

property num_envs

Retrieves number of environments.

Returns

Number of environments.

Return type

num_envs(int)

observation_space: gym.spaces.space.Space[gym.core.ObsType]

render(mode='human') → None

Step the renderer.

Parameters

mode (str) – Select mode of rendering based on OpenAI environments.

render_mode: Optional[str] = None

reset()

Resets the task and updates observations.

reward_range = (-inf, inf)

run(trainer)

Main loop for controlling simulation and task stepping. This method is responsible for starting simulation, stepping task and simulation, collecting buffers from task, sending data to policy, and retrieving actions from policy. It also deals with the case when the policy terminates on completion and continues the simulation thread so that UI does not get affected.

Parameters

trainer (TrainerMT) – A Trainer object that implements APIs for starting and stopping RL thread.

seed(seed=- 1)

Sets a seed. Pass in -1 for a random seed.

Parameters

seed (int) – Seed to set. Defaults to -1.

Returns

Seed that was set.

Return type

seed (int)

send_actions(actions, block=True)

Sends actions from RL thread to simulation thread by adding actions to queue.

Parameters

• actions (Union[np.ndarray, torch.Tensor]) – actions buffer to be added to queue.

• block (Optional[bool]) – Whether to block thread when writing to queue.

send_data(data, block=True)

Sends data from task thread to RL thread by adding data to queue.

Parameters

• data (dict) – Dictionary containing task data.

• block (Optional[bool]) – Whether to block thread when writing to queue.

set_task(task, backend='numpy', sim_params=None, init_sim=True) → None

Creates a World object and adds Task to World.

Initializes and registers task to the environment interface. Triggers task start-up.

Parameters

• task (RLTask) – The task to register to the env.

• backend (str) – Backend to use for task. Can be “numpy” or “torch”. Defaults to “numpy”.

• sim_params (dict) – Simulation parameters for physics settings. Defaults to None.

• init_sim (Optional[bool]) – Automatically starts simulation. Defaults to True.

spec: EnvSpec = None

step(actions)

Basic implementation for stepping simulation.

Can be overriden by inherited Env classes to satisfy requirements of specific RL libraries. This method passes actions to task for processing, steps simulation, and computes observations, rewards, and resets.

Parameters

actions (Union[numpy.ndarray, torch.Tensor]) – Actions buffer from policy.

Returns

Buffer of observation data. rewards(Union[numpy.ndarray, torch.Tensor]): Buffer of rewards data. dones(Union[numpy.ndarray, torch.Tensor]): Buffer of resets/dones data. info(dict): Dictionary of extras data.

Return type

observations(Union[numpy.ndarray, torch.Tensor])

property unwrapped: gym.core.Env

Returns the base non-wrapped environment.

Returns

The base non-wrapped gym.Env instance

Return type

Env