Update vector env docs (#1053)

gymnasium/vector/vector_env.py

@@ -30,32 +30,45 @@ class VectorEnv(Generic[ObsType, ActType, ArrayType]):
  """Base class for vectorized environments to run multiple independent copies of the same environment in parallel.
 
  Vector environments can provide a linear speed-up in the steps taken per second through sampling multiple
- sub-environments at the same time. To prevent terminated environments waiting until all sub-environments have
- terminated or truncated, the vector environments automatically reset sub-environments after they terminate or truncated (within the same step call).
- As a result, the step's observation and info are overwritten by the reset's observation and info.
- To preserve this data, the observation and info for the final step of a sub-environment is stored in the info parameter,
- using `"final_observation"` and `"final_info"` respectively. See :meth:`step` for more information.
+ sub-environments at the same time. Gymnasium contains two generalised Vector environments: :class:`AsyncVectorEnv`
+ and :class:`SyncVectorEnv` along with several custom vector environment implementations.
+ For :func:`reset` and :func:`step` batches `observations`, `rewards`, `terminations`, `truncations` and
+ `info` for each sub-environment, see the example below. For the `rewards`, `terminations`, and `truncations`,
+ the data is packaged into a NumPy array of shape `(num_envs,)`. For `observations` (and `actions`, the batching
+ process is dependent on the type of observation (and action) space, and generally optimised for neural network
+ input/outputs. For `info`, the data is kept as a dictionary such that a key will give the data for all sub-environment.
+
+ For creating environments, :func:`make_vec` is a vector environment equivalent to :func:`make` for easily creating
+ vector environments that contains several unique arguments for modifying environment qualities, number of environment,
+ vectorizer type, vectorizer arguments.
 
- The vector environments batches `observations`, `rewards`, `terminations`, `truncations` and `info` for each
- sub-environment. In addition, :meth:`step` expects to receive a batch of actions for each parallel environment.
-
- Gymnasium contains two generalised Vector environments: :class:`AsyncVectorEnv` and :class:`SyncVectorEnv` along with
- several custom vector environment implementations.
-
- The Vector Environments have the additional attributes for users to understand the implementation
-
- - :attr:`num_envs` - The number of sub-environment in the vector environment
- - :attr:`observation_space` - The batched observation space of the vector environment
- - :attr:`single_observation_space` - The observation space of a single sub-environment
- - :attr:`action_space` - The batched action space of the vector environment
- - :attr:`single_action_space` - The action space of a single sub-environment
+ Note:
+ The info parameter of :meth:`reset` and :meth:`step` was originally implemented before v0.25 as a list
+ of dictionary for each sub-environment. However, this was modified in v0.25+ to be a dictionary with a NumPy
+ array for each key. To use the old info style, utilise the :class:`DictInfoToList` wrapper.
 
  Examples:
  >>> import gymnasium as gym
  >>> envs = gym.make_vec("CartPole-v1", num_envs=3, vectorization_mode="sync", wrappers=(gym.wrappers.TimeAwareObservation,))
  >>> envs = gym.wrappers.vector.ClipReward(envs, min_reward=0.2, max_reward=0.8)
  >>> envs
  <ClipReward, SyncVectorEnv(CartPole-v1, num_envs=3)>
+ >>> envs.num_envs
+ 3
+ >>> envs.action_space
+ MultiDiscrete([2 2 2])
+ >>> envs.observation_space
+ Box([[-4.80000019e+00 -3.40282347e+38 -4.18879032e-01 -3.40282347e+38
+ 0.00000000e+00]
+ [-4.80000019e+00 -3.40282347e+38 -4.18879032e-01 -3.40282347e+38
+ 0.00000000e+00]
+ [-4.80000019e+00 -3.40282347e+38 -4.18879032e-01 -3.40282347e+38
+ 0.00000000e+00]], [[4.80000019e+00 3.40282347e+38 4.18879032e-01 3.40282347e+38
+ 5.00000000e+02]
+ [4.80000019e+00 3.40282347e+38 4.18879032e-01 3.40282347e+38
+ 5.00000000e+02]
+ [4.80000019e+00 3.40282347e+38 4.18879032e-01 3.40282347e+38
+ 5.00000000e+02]], (3, 5), float64)
  >>> observations, infos = envs.reset(seed=123)
  >>> observations
  array([[ 0.01823519, -0.0446179 , -0.02796401, -0.03156282, 0. ],
@@ -64,7 +77,8 @@ class VectorEnv(Generic[ObsType, ActType, ArrayType]):
  >>> infos
  {}
  >>> _ = envs.action_space.seed(123)
- >>> observations, rewards, terminations, truncations, infos = envs.step(envs.action_space.sample())
+ >>> actions = envs.action_space.sample()
+ >>> observations, rewards, terminations, truncations, infos = envs.step(actions)
  >>> observations
  array([[ 0.01734283, 0.15089367, -0.02859527, -0.33293587, 1. ],
  [ 0.02909703, -0.16717631, 0.04740972, 0.3319138 , 1. ],
@@ -79,17 +93,18 @@ class VectorEnv(Generic[ObsType, ActType, ArrayType]):
  {}
  >>> envs.close()
 
- Note:
-  The info parameter of :meth:`reset` and :meth:`step` was originally implemented before v0.25 as a list
-  of dictionary for each sub-environment. However, this was modified in v0.25+ to be a
-  dictionary with a NumPy array for each key. To use the old info style, utilise the :class:`DictInfoToList` wrapper.
+ To avoid having to wait for all sub-environments to terminated before resetting, implementations will autoreset
+ sub-environments on episode end (`terminated or truncated is True`). As a result, when adding observations
+ to a replay buffer, this requires a knowning where the observation (and info) for each sub-environment are the first
+ observation from an autoreset. We recommend using an additional variable to store this information.
 
- Note:
- All parallel environments should share the identical observation and action spaces.
- In other words, a vector of multiple different environments is not supported.
+ The Vector Environments have the additional attributes for users to understand the implementation
 
- Note:
- :func:`make_vec` is the equivalent function to :func:`make` for vector environments.
+ - :attr:`num_envs` - The number of sub-environment in the vector environment
+ - :attr:`observation_space` - The batched observation space of the vector environment
+ - :attr:`single_observation_space` - The observation space of a single sub-environment
+ - :attr:`action_space` - The batched action space of the vector environment
+ - :attr:`single_action_space` - The action space of a single sub-environment
  """
 
  metadata: dict[str, Any] = {}
@@ -149,9 +164,8 @@ def step(
  Batch of (observations, rewards, terminations, truncations, infos)
 
  Note:
- As the vector environments autoreset for a terminating and truncating sub-environments,
- the returned observation and info is not the final step's observation or info which is instead stored in
- info as `"final_observation"` and `"final_info"`.
+ As the vector environments autoreset for a terminating and truncating sub-environments, this will occur on
+ the next step after `terminated or truncated is True`.
 
  Example:
  >>> import gymnasium as gym