Vector wrappers#

Vector only wrappers#

class gymnasium.wrappers.vector.DictInfoToList(env: VectorEnv)[source]#

Converts infos of vectorized environments from dict to List[dict].

This wrapper converts the info format of a vector environment from a dictionary to a list of dictionaries. This wrapper is intended to be used around vectorized environments. If using other wrappers that perform operation on info like RecordEpisodeStatistics this need to be the outermost wrapper.

i.e. DictInfoToList(RecordEpisodeStatistics(vector_env))

Example

>>> import numpy as np
>>> dict_info = {
...      "k": np.array([0., 0., 0.5, 0.3]),
...      "_k": np.array([False, False, True, True])
...  }
...
>>> list_info = [{}, {}, {"k": 0.5}, {"k": 0.3}]
Example for vector environments:
>>> import numpy as np
>>> import gymnasium as gym
>>> from gymnasium.spaces import Dict, Box
>>> envs = gym.make_vec("CartPole-v1", num_envs=3)
>>> obs, info = envs.reset(seed=123)
>>> info
{}
>>> envs = DictInfoToList(envs)
>>> obs, info = envs.reset(seed=123)
>>> info
[{}, {}, {}]
Another example for vector environments:
>>> import numpy as np
>>> import gymnasium as gym
>>> envs = gym.make_vec("HalfCheetah-v4", num_envs=3)
>>> _ = envs.reset(seed=123)
>>> _ = envs.action_space.seed(123)
>>> _, _, _, _, infos = envs.step(envs.action_space.sample())
>>> infos
{'x_position': array([0.03332211, 0.10172355, 0.08920531]), '_x_position': array([ True,  True,  True]), 'x_velocity': array([-0.06296527,  0.89345848,  0.37710836]), '_x_velocity': array([ True,  True,  True]), 'reward_run': array([-0.06296527,  0.89345848,  0.37710836]), '_reward_run': array([ True,  True,  True]), 'reward_ctrl': array([-0.24503503, -0.21944423, -0.20672209]), '_reward_ctrl': array([ True,  True,  True])}
>>> envs = DictInfoToList(envs)
>>> _ = envs.reset(seed=123)
>>> _ = envs.action_space.seed(123)
>>> _, _, _, _, infos = envs.step(envs.action_space.sample())
>>> infos
[{'x_position': 0.03332210900362942, 'x_velocity': -0.06296527291998533, 'reward_run': -0.06296527291998533, 'reward_ctrl': -0.2450350284576416}, {'x_position': 0.10172354684460168, 'x_velocity': 0.8934584807363618, 'reward_run': 0.8934584807363618, 'reward_ctrl': -0.21944422721862794}, {'x_position': 0.08920531470057845, 'x_velocity': 0.3771083596080768, 'reward_run': 0.3771083596080768, 'reward_ctrl': -0.20672209262847902}]
Change logs:

  • v0.24.0 - Initially added as VectorListInfo

  • v1.0.0 - Renamed to DictInfoToList

Parameters:

env (Env) – The environment to apply the wrapper

Vectorize Transform Wrappers to Vector Wrappers#

class gymnasium.wrappers.vector.VectorizeTransformObservation(env: VectorEnv, wrapper: type[TransformObservation], **kwargs: Any)[source]#

Vectorizes a single-agent transform observation wrapper for vector environments.

Most of the lambda observation wrappers for single agent environments have vectorized implementations, it is advised that users simply use those instead via importing from gymnasium.wrappers.vector…. The following example illustrate use-cases where a custom lambda observation wrapper is required.

Example - The normal observation:
>>> import gymnasium as gym
>>> envs = gym.make_vec("CartPole-v1", num_envs=3, vectorization_mode="sync")
>>> obs, info = envs.reset(seed=123)
>>> envs.close()
>>> obs
array([[ 0.01823519, -0.0446179 , -0.02796401, -0.03156282],
       [ 0.02852531,  0.02858594,  0.0469136 ,  0.02480598],
       [ 0.03517495, -0.000635  , -0.01098382, -0.03203924]],
      dtype=float32)
Example - Applying a custom lambda observation wrapper that duplicates the observation from the environment
>>> import numpy as np
>>> import gymnasium as gym
>>> from gymnasium.spaces import Box
>>> from gymnasium.wrappers import TransformObservation
>>> envs = gym.make_vec("CartPole-v1", num_envs=3, vectorization_mode="sync")
>>> old_space = envs.single_observation_space
>>> new_space = Box(low=np.array([old_space.low, old_space.low]), high=np.array([old_space.high, old_space.high]))
>>> envs = VectorizeTransformObservation(envs, wrapper=TransformObservation, func=lambda x: np.array([x, x]), observation_space=new_space)
>>> obs, info = envs.reset(seed=123)
>>> envs.close()
>>> obs
array([[[ 0.01823519, -0.0446179 , -0.02796401, -0.03156282],
        [ 0.01823519, -0.0446179 , -0.02796401, -0.03156282]],

       [[ 0.02852531,  0.02858594,  0.0469136 ,  0.02480598],
        [ 0.02852531,  0.02858594,  0.0469136 ,  0.02480598]],

       [[ 0.03517495, -0.000635  , -0.01098382, -0.03203924],
        [ 0.03517495, -0.000635  , -0.01098382, -0.03203924]]],
      dtype=float32)
Parameters:

  • env – The vector environment to wrap.

  • wrapper – The wrapper to vectorize

  • **kwargs – Keyword argument for the wrapper

class gymnasium.wrappers.vector.VectorizeTransformAction(env: VectorEnv, wrapper: type[TransformAction], **kwargs: Any)[source]#

Vectorizes a single-agent transform action wrapper for vector environments.

Example - Without action transformation:
>>> import gymnasium as gym
>>> envs = gym.make_vec("MountainCarContinuous-v0", num_envs=3)
>>> _ = envs.action_space.seed(123)
>>> obs, info = envs.reset(seed=123)
>>> obs, rew, term, trunc, info = envs.step(envs.action_space.sample())
>>> envs.close()
>>> obs
array([[-4.6343064e-01,  9.8971417e-05],
       [-4.4488689e-01, -1.9375233e-03],
       [-4.3118435e-01, -1.5342437e-03]], dtype=float32)
Example - Adding a transform that applies a ReLU to the action:
>>> import gymnasium as gym
>>> from gymnasium.wrappers import TransformAction
>>> envs = gym.make_vec("MountainCarContinuous-v0", num_envs=3)
>>> envs = VectorizeTransformAction(envs, wrapper=TransformAction, func=lambda x: (x > 0.0) * x, action_space=envs.single_action_space)
>>> _ = envs.action_space.seed(123)
>>> obs, info = envs.reset(seed=123)
>>> obs, rew, term, trunc, info = envs.step(envs.action_space.sample())
>>> envs.close()
>>> obs
array([[-4.6343064e-01,  9.8971417e-05],
       [-4.4354835e-01, -5.9898634e-04],
       [-4.3034542e-01, -6.9532328e-04]], dtype=float32)
Parameters:

  • env – The vector environment to wrap

  • wrapper – The wrapper to vectorize

  • **kwargs – Arguments for the LambdaAction wrapper

class gymnasium.wrappers.vector.VectorizeTransformReward(env: VectorEnv, wrapper: type[TransformReward], **kwargs: Any)[source]#

Vectorizes a single-agent transform reward wrapper for vector environments.

Example

Without reward transformation: >>> import gymnasium as gym >>> envs = gym.make_vec(“MountainCarContinuous-v0”, num_envs=3) >>> _ = envs.action_space.seed(123) >>> obs, info = envs.reset(seed=123) >>> obs, rew, term, trunc, info = envs.step(envs.action_space.sample()) >>> envs.close() >>> rew array([-0.01330088, -0.07963027, -0.03127944])

Adding a transform that applies a ReLU to the reward: >>> import gymnasium as gym >>> from gymnasium.wrappers import TransformReward >>> envs = gym.make_vec(“MountainCarContinuous-v0”, num_envs=3) >>> envs = VectorizeTransformReward(envs, wrapper=TransformReward, func=lambda x: (x > 0.0) * x) >>> _ = envs.action_space.seed(123) >>> obs, info = envs.reset(seed=123) >>> obs, rew, term, trunc, info = envs.step(envs.action_space.sample()) >>> envs.close() >>> rew array([-0., -0., -0.])

Parameters:

  • env – The vector environment to wrap.

  • wrapper – The wrapper to vectorize

  • **kwargs – Keyword argument for the wrapper