mirror of
https://github.com/Farama-Foundation/Gymnasium.git
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Add support for python doc files without rst gallery (#770)
Co-authored-by: Mark Towers <mark.m.towers@gmail.com>
This commit is contained in:
@@ -16,6 +16,7 @@ import re
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import sys
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import sphinx_gallery.gen_rst
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from furo.gen_tutorials import generate_tutorials
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# Path setup for building from source tree
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@@ -138,6 +139,12 @@ sphinx_gallery_conf = {
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),
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}
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# All tutorials in the tutorials directory will be generated automatically
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# by sphinx-gallery.
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# However, we also want to generate some tutorials without the gallery
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# and to a more specific location so we use this custom function.
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generate_tutorials("introduction/*.py", "./introduction")
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# -- Generate Changelog -------------------------------------------------
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sphinx_github_changelog_token = os.environ.get("SPHINX_GITHUB_CHANGELOG_TOKEN")
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192
docs/index.md
192
docs/index.md
@@ -1,96 +1,96 @@
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---
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hide-toc: true
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firstpage:
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lastpage:
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---
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```{project-logo} _static/img/gymnasium-text.png
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:alt: Gymnasium Logo
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```
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```{project-heading}
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An API standard for reinforcement learning with a diverse collection of reference environments
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```
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```{figure} _static/videos/box2d/lunar_lander.gif
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:alt: Lunar Lander
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:width: 500
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```
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**Gymnasium is a maintained fork of OpenAI’s Gym library.** The Gymnasium interface is simple, pythonic, and capable of representing general RL problems, and has a [compatibility wrapper](introduction/gym_compatibility) for old Gym environments:
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```{code-block} python
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import gymnasium as gym
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# Initialise the environment
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env = gym.make("LunarLander-v2", render_mode="human")
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# Reset the environment to generate the first observation
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observation, info = env.reset(seed=42)
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for _ in range(1000):
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# this is where you would insert your policy
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action = env.action_space.sample()
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# step (transition) through the environment with the action
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# receiving the next observation, reward and if the episode has terminated or truncated
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observation, reward, terminated, truncated, info = env.step(action)
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# If the episode has ended then we can reset to start a new episode
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if terminated or truncated:
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observation, info = env.reset()
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env.close()
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```
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```{toctree}
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:hidden:
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:caption: Introduction
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introduction/basic_usage
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introduction/gym_compatibility
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introduction/migration-guide
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```
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```{toctree}
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:hidden:
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:caption: API
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api/env
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api/registry
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api/spaces
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api/wrappers
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api/vector
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api/utils
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api/functional
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```
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```{toctree}
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:hidden:
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:caption: Environments
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environments/classic_control
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environments/box2d
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environments/toy_text
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environments/mujoco
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environments/atari
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environments/third_party_environments
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```
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```{toctree}
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:hidden:
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:glob:
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:caption: Tutorials
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tutorials/**/index
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Comet Tutorial <https://www.comet.com/docs/v2/integrations/ml-frameworks/gymnasium/?utm_source=gymnasium&utm_medium=partner&utm_campaign=partner_gymnasium_2023&utm_content=docs_gymnasium>
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```
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```{toctree}
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:hidden:
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:caption: Development
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Github <https://github.com/Farama-Foundation/Gymnasium>
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gymnasium_release_notes/index
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gym_release_notes/index
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Contribute to the Docs <https://github.com/Farama-Foundation/Gymnasium/blob/main/docs/README.md>
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```
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---
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hide-toc: true
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firstpage:
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lastpage:
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---
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```{project-logo} _static/img/gymnasium-text.png
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:alt: Gymnasium Logo
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```
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```{project-heading}
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An API standard for reinforcement learning with a diverse collection of reference environments
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```
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```{figure} _static/videos/box2d/lunar_lander.gif
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:alt: Lunar Lander
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:width: 500
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```
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**Gymnasium is a maintained fork of OpenAI’s Gym library.** The Gymnasium interface is simple, pythonic, and capable of representing general RL problems, and has a [compatibility wrapper](introduction/gym_compatibility) for old Gym environments:
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```{code-block} python
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import gymnasium as gym
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# Initialise the environment
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env = gym.make("LunarLander-v2", render_mode="human")
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# Reset the environment to generate the first observation
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observation, info = env.reset(seed=42)
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for _ in range(1000):
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# this is where you would insert your policy
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action = env.action_space.sample()
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# step (transition) through the environment with the action
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# receiving the next observation, reward and if the episode has terminated or truncated
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observation, reward, terminated, truncated, info = env.step(action)
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# If the episode has ended then we can reset to start a new episode
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if terminated or truncated:
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observation, info = env.reset()
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env.close()
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```
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```{toctree}
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:hidden:
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:caption: Introduction
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introduction/basic_usage
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introduction/gym_compatibility
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introduction/migration-guide
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```
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```{toctree}
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:hidden:
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:caption: API
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api/env
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api/registry
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api/spaces
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api/wrappers
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api/vector
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api/utils
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api/functional
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```
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```{toctree}
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:hidden:
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:caption: Environments
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environments/classic_control
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environments/box2d
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environments/toy_text
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environments/mujoco
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environments/atari
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environments/third_party_environments
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```
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```{toctree}
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:hidden:
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:glob:
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:caption: Tutorials
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tutorials/**/index
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Comet Tutorial <https://www.comet.com/docs/v2/integrations/ml-frameworks/gymnasium/?utm_source=gymnasium&utm_medium=partner&utm_campaign=partner_gymnasium_2023&utm_content=docs_gymnasium>
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```
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```{toctree}
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:hidden:
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:caption: Development
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Github <https://github.com/Farama-Foundation/Gymnasium>
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gymnasium_release_notes/index
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gym_release_notes/index
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Contribute to the Docs <https://github.com/Farama-Foundation/Gymnasium/blob/main/docs/README.md>
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```
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