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Updates the Env, Wrapper and Vector API documentation (#48)

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Mark Towers
2022-10-12 15:58:01 +01:00
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parent 9070295cc9
commit 2a7ebc4271
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docs/api/vector.md
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@@ -4,15 +4,26 @@ title: Vector
# Vector
```{eval-rst}
.. autofunction:: gymnasium.vector.make
```
## VectorEnv
## Gymnasium.vector.VectorEnv
```{eval-rst}
.. attribute:: gymnasium.vector.VectorEnv.action_space
.. autoclass:: gymnasium.vector.VectorEnv
```
### Methods
```{eval-rst}
.. automethod:: gymnasium.vector.VectorEnv.reset
.. automethod:: gymnasium.vector.VectorEnv.step
.. automethod:: gymnasium.vector.VectorEnv.close
```
### Attributes
```{eval-rst}
.. attribute:: action_space
The (batched) action space. The input actions of `step` must be valid elements of `action_space`.::
@@ -20,7 +31,7 @@ title: Vector
>>> envs.action_space
MultiDiscrete([2 2 2])
.. attribute:: gymnasium.vector.VectorEnv.observation_space
.. attribute:: observation_space
The (batched) observation space. The observations returned by `reset` and `step` are valid elements of `observation_space`.::
@@ -28,7 +39,7 @@ title: Vector
>>> envs.observation_space
Box([[-4.8 ...]], [[4.8 ...]], (3, 4), float32)
.. attribute:: gymnasium.vector.VectorEnv.single_action_space
.. attribute:: single_action_space
The action space of an environment copy.::
@@ -36,55 +47,29 @@ title: Vector
>>> envs.single_action_space
Discrete(2)
.. attribute:: gymnasium.vector.VectorEnv.single_observation_space
.. attribute:: single_observation_space
The observation space of an environment copy.::
>>> envs = gymnasium.vector.make("CartPole-v1", num_envs=3)
>>> envs.single_action_space
Box([-4.8 ...], [4.8 ...], (4,), float32)
```
```
### Reset
## Making Vector Environments
```{eval-rst}
.. automethod:: gymnasium.vector.VectorEnv.reset
```
```python
>>> import gymnasium as gym
>>> envs = gym.vector.make("CartPole-v1", num_envs=3)
>>> envs.reset()
(array([[-0.02240574, -0.03439831, -0.03904812, 0.02810693],
[ 0.01586068, 0.01929009, 0.02394426, 0.04016077],
[-0.01314174, 0.03893502, -0.02400815, 0.0038326 ]],
dtype=float32), {})
.. autofunction:: gymnasium.vector.make
```
### Step
## Async Vector Env
```{eval-rst}
.. automethod:: gymnasium.vector.VectorEnv.step
```
```python
>>> envs = gym.vector.make("CartPole-v1", num_envs=3)
>>> envs.reset()
>>> actions = np.array([1, 0, 1])
>>> observations, rewards, termination, truncation, infos = envs.step(actions)
>>> observations
array([[ 0.00122802, 0.16228443, 0.02521779, -0.23700266],
[ 0.00788269, -0.17490888, 0.03393489, 0.31735462],
[ 0.04918966, 0.19421194, 0.02938497, -0.29495203]],
dtype=float32)
>>> rewards
array([1., 1., 1.])
>>> termination
array([False, False, False])
>>> termination
array([False, False, False])
>>> infos
{}
.. autoclass:: gymnasium.vector.AsyncVectorEnv
```
## Sync Vector Env
```{eval-rst}
.. autoclass:: gymnasium.vector.SyncVectorEnv
```