Make pre-commit match Gymnasium (add many more pre-commit hook checks)

.github/ISSUE_TEMPLATE/question.yml

@@ -6,13 +6,13 @@ body:
   - type: markdown
     attributes:
       value: >
-        If you have basic questions about reinforcement learning algorithms, please ask on 
-        [r/reinforcementlearning](https://www.reddit.com/r/reinforcementlearning/) or in the 
-        [RL Discord](https://discord.com/invite/xhfNqQv) (if you're new please use the beginners channel). 
-        Basic questions that are not bugs or feature requests will be closed without reply, because GitHub 
-        issues are not an appropriate venue for these. Advanced/nontrivial questions, especially in areas where 
+        If you have basic questions about reinforcement learning algorithms, please ask on
+        [r/reinforcementlearning](https://www.reddit.com/r/reinforcementlearning/) or in the
+        [RL Discord](https://discord.com/invite/xhfNqQv) (if you're new please use the beginners channel).
+        Basic questions that are not bugs or feature requests will be closed without reply, because GitHub
+        issues are not an appropriate venue for these. Advanced/nontrivial questions, especially in areas where
         documentation is lacking, are very much welcome.
-        
+
   - type: textarea
     id: question
     attributes:

.pre-commit-config.yaml

@@ -1,5 +1,24 @@
----
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
 repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.4.0
+    hooks:
+      - id: check-symlinks
+      - id: destroyed-symlinks
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-yaml
+      - id: check-toml
+      - id: check-ast
+      - id: check-added-large-files
+      - id: check-merge-conflict
+      - id: check-executables-have-shebangs
+      - id: check-shebang-scripts-are-executable
+      - id: detect-private-key
+      - id: debug-statements
+      - id: mixed-line-ending
+        args: [ "--fix=lf" ]
   - repo: https://github.com/python/black
     rev: 23.3.0
     hooks:
@@ -28,15 +47,10 @@ repos:
       - id: isort
         args: ["--profile", "black"]
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.3.1
+    rev: v3.3.2
     hooks:
       - id: pyupgrade
         args: ["--py37-plus"]
-  - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
-    hooks:
-      - id: mixed-line-ending
-        args: ["--fix=lf"]
   - repo: https://github.com/pycqa/pydocstyle
     rev: 6.3.0
     hooks:
@@ -59,3 +73,5 @@ repos:
         pass_filenames: false
         types: [python]
         additional_dependencies: ["pyright"]
+        args:
+          - --project=pyproject.toml

CODE_OF_CONDUCT.rst

@@ -65,4 +65,3 @@ Attribution
 -----------
 This Code of Conduct is adapted from `Python's Code of Conduct <https://www.python.org/psf/conduct/>`_, which is under a `Creative Commons License
 <https://creativecommons.org/licenses/by-sa/3.0/>`_.
-

CONTRIBUTING.md

@@ -58,7 +58,7 @@ Tutorials are a crucial way to help people learn how to use PettingZoo and we gr
 - You should make a `.md` file for each tutorial within the above directory.
 - Each `.md` file should have an "Environment Setup" section and a "Code" section. The title should be of the format `<TUTORIAL_THEME>: <TUTORIAL_TOPIC>`.
 - The Environment Setup section should reference the `requirements.txt` file you created using `literalinclude`.
-- The Code section should reference the `.py` file you created using `literalinclude`. 
+- The Code section should reference the `.py` file you created using `literalinclude`.
 - `/docs/index.md` should be modified to include every new tutorial.
 
 ### Testing your tutorial

LICENSE

@@ -1,13 +1,13 @@
 This repository is licensed as follows:
-All assets in this repository are the copyright of the Farama Foundation, except 
+All assets in this repository are the copyright of the Farama Foundation, except
 where prohibited. Contributors to the repository transfer copyright of their work
 to the Farama Foundation.
 
 Some code in this repository has been taken from other open source projects
 and was originally released under the MIT or Apache 2.0 licenses, with
 copyright held by another party. We've attributed these authors and they
 retain their copyright to the extent required by law. Everything else
-is owned by the Farama Foundation. The Secret Code font was also released under 
+is owned by the Farama Foundation. The Secret Code font was also released under
 the MIT license by Matthew Welch (http://www.squaregear.net/fonts/).
 The MIT and Apache 2.0 licenses are included below.
 

README.md

@@ -20,15 +20,15 @@ PettingZoo includes the following families of environments:
 
 To install the base PettingZoo library: `pip install pettingzoo`.
 
-This does not include dependencies for all families of environments (some environments can be problematic to install on certain systems). 
+This does not include dependencies for all families of environments (some environments can be problematic to install on certain systems).
 
 To install the dependencies for one family, use `pip install pettingzoo[atari]`, or use `pip install pettingzoo[all]` to install all dependencies.
 
 We support Python 3.7, 3.8, 3.9 and 3.10 on Linux and macOS. We will accept PRs related to Windows, but do not officially support it.
 
 ## Getting started
 
-For an introduction to PettingZoo, see [Basic Usage](https://pettingzoo.farama.org/content/basic_usage/). To create a new environment, see our [Environment Creation Tutorial](https://pettingzoo.farama.org/tutorials/environmentcreation/1-project-structure/) and [Custom Environment Examples](https://pettingzoo.farama.org/content/environment_creation/). 
+For an introduction to PettingZoo, see [Basic Usage](https://pettingzoo.farama.org/content/basic_usage/). To create a new environment, see our [Environment Creation Tutorial](https://pettingzoo.farama.org/tutorials/environmentcreation/1-project-structure/) and [Custom Environment Examples](https://pettingzoo.farama.org/content/environment_creation/).
 For examples of training RL models using PettingZoo see our tutorials:
 * [CleanRL: Implementing PPO](https://pettingzoo.farama.org/tutorials/cleanrl/implementing_PPO/):train multiple PPO agents in the [Pistonball](https://pettingzoo.farama.org/environments/butterfly/pistonball/) environment.
 * [Tianshou: Training Agents](https://pettingzoo.farama.org/tutorials/tianshou/intermediate/): train DQN agents in the [Tic-Tac-Toe](https://pettingzoo.farama.org/environments/classic/tictactoe/) environment.

docs/api/aec.md

@@ -24,20 +24,20 @@ env.reset(seed=42)
 
 for agent in env.agent_iter():
     observation, reward, termination, truncation, info = env.last()
-    
+
     if termination or truncation:
         action = None
-    else:    
+    else:
         action = env.action_space(agent).sample() # this is where you would insert your policy
-    
-    env.step(action) 
+
+    env.step(action)
 env.close()
 ```
 
 ### Action Masking
-AEC environments often include action masks, in order to mark valid/invalid actions for the agent. 
+AEC environments often include action masks, in order to mark valid/invalid actions for the agent.
 
-To sample actions using action masking: 
+To sample actions using action masking:
 ```python
 from pettingzoo.classic import chess_v6
 
@@ -49,17 +49,17 @@ for agent in env.agent_iter():
 
     if termination or truncation:
         action = None
-    else:  
+    else:
         # invalid action masking is optional and environment-dependent
         if "action_mask" in info:
             mask = info["action_mask"]
         elif isinstance(observation, dict) and "action_mask" in observation:
             mask = observation["action_mask"]
         else:
-            mask = None 
+            mask = None
         action = env.action_space(agent).sample(mask) # this is where you would insert your policy
-        
-    env.step(action) 
+
+    env.step(action)
 env.close()
 ```
 
@@ -68,7 +68,7 @@ Note: action masking is optional, and can be implemented using either `observati
 * [PettingZoo Classic](https://pettingzoo.farama.org/environments/classic/) environments store action masks in the `observation` dict:
   * `mask = observation["action_mask"]`
 * [Shimmy](https://shimmy.farama.org/)'s [OpenSpiel environments](https://shimmy.farama.org/environments/open_spiel/) stores action masks in the `info` dict:
-  * `mask = info["action_mask"]` 
+  * `mask = info["action_mask"]`
 
 To implement action masking in a custom environment, see [Environment Creation: Action Masking](https://pettingzoo.farama.org/tutorials/environmentcreation/3-action-masking/)
 
@@ -158,4 +158,3 @@ For more information on action masking, see [A Closer Look at Invalid Action Mas
 .. automethod:: AECEnv.close
 
 ```
-

docs/api/parallel.md

@@ -22,8 +22,8 @@ observations = parallel_env.reset(seed=42)
 
 while env.agents:
     # this is where you would insert your policy
-    actions = {agent: parallel_env.action_space(agent).sample() for agent in parallel_env.agents}  
-    
+    actions = {agent: parallel_env.action_space(agent).sample() for agent in parallel_env.agents}
+
     observations, rewards, terminations, truncations, infos = parallel_env.step(actions)
 env.close()
 ```

docs/api/wrappers.md

@@ -6,7 +6,7 @@ title: Wrapper
 
 ## Using Wrappers
 
-A wrapper is an environment transformation that takes in an environment as input, and outputs a new environment that is similar to the input environment, but with some transformation or validation applied. 
+A wrapper is an environment transformation that takes in an environment as input, and outputs a new environment that is similar to the input environment, but with some transformation or validation applied.
 
 The following wrappers can be used with PettingZoo environments:
 
@@ -16,12 +16,12 @@ The following wrappers can be used with PettingZoo environments:
 
 [Supersuit Wrappers](/api/wrappers/supersuit_wrappers/) include commonly used pre-processing functions such as frame-stacking and color reduction, compatible with both PettingZoo and Gymnasium.
 
-[Shimmy Compatibility Wrappers](/api/wrappers/shimmy_wrappers/) allow commonly used external reinforcement learning environments to be used with PettingZoo and Gymnasium. 
+[Shimmy Compatibility Wrappers](/api/wrappers/shimmy_wrappers/) allow commonly used external reinforcement learning environments to be used with PettingZoo and Gymnasium.
 
 
 ```{toctree}
 :hidden:
 wrappers/pz_wrappers
 wrappers/supersuit_wrappers
 wrappers/shimmy_wrappers
-```
+```

docs/api/wrappers/pz_wrappers.md

@@ -4,7 +4,7 @@ title: PettingZoo Wrappers
 
 # PettingZoo Wrappers
 
-PettingZoo includes the following types of wrappers: 
+PettingZoo includes the following types of wrappers:
 * [Conversion Wrappers](#conversion-wrappers): wrappers for converting environments between the [AEC](/api/aec/) and [Parallel](/api/parallel/) APIs
 * [Utility Wrappers](#utility-wrappers): a set of wrappers which provide convenient reusable logic, such as enforcing turn order or clipping out-of-bounds actions.
 
@@ -105,4 +105,4 @@ Note: Most AEC environments include TerminateIllegalWrapper in their initializat
 .. autoclass:: ClipOutOfBoundsWrapper
 .. autoclass:: OrderEnforcingWrapper
 
-```
+```

docs/api/wrappers/supersuit_wrappers.md

@@ -4,7 +4,7 @@ title: Supersuit Wrappers
 
 # Supersuit Wrappers
 
-The [SuperSuit](https://github.com/Farama-Foundation/SuperSuit) companion package (`pip install supersuit`) includes a collection of pre-processing functions which can applied to both [AEC](/api/aec/) and [Parallel](/api/parallel/) environments. 
+The [SuperSuit](https://github.com/Farama-Foundation/SuperSuit) companion package (`pip install supersuit`) includes a collection of pre-processing functions which can applied to both [AEC](/api/aec/) and [Parallel](/api/parallel/) environments.
 
 To convert [space invaders](https://pettingzoo.farama.org/environments/atari/space_invaders/) to a greyscale observation space and stack the last 4 frames:
 

docs/content/basic_usage.md

@@ -7,7 +7,7 @@ title: API
 
 To install the base PettingZoo library: `pip install pettingzoo`.
 
-This does not include dependencies for all families of environments (some environments can be problematic to install on certain systems). 
+This does not include dependencies for all families of environments (some environments can be problematic to install on certain systems).
 
 To install the dependencies for one family, use `pip install pettingzoo[atari]`, or use `pip install pettingzoo[all]` to install all dependencies.
 

docs/environments/atari.md

@@ -66,8 +66,8 @@ for agent in env.agent_iter():
         action = None
     else:
         action = env.action_space(agent).sample() # this is where you would insert your policy
-        
-    env.step(action) 
+
+    env.step(action)
 env.close()
 ```
 

docs/environments/butterfly.md

@@ -16,7 +16,7 @@ butterfly/pistonball
     :file: butterfly/list.html
 ```
 
-Butterfly environments are challenging scenarios created by Farama, using Pygame with visual Atari spaces. 
+Butterfly environments are challenging scenarios created by Farama, using Pygame with visual Atari spaces.
 
 All environments require a high degree of coordination and require learning of emergent behaviors to achieve an optimal policy. As such, these environments are currently very challenging to learn.
 
@@ -25,7 +25,7 @@ Environments are highly configurable via arguments specified in their respective
 [Knights Archers Zombies](https://pettingzoo.farama.org/environments/butterfly/knights_archers_zombies/),
 [Pistonball](https://pettingzoo.farama.org/environments/butterfly/pistonball/).
 
-### Installation 
+### Installation
 The unique dependencies for this set of environments can be installed via:
 
 ````bash
@@ -43,7 +43,7 @@ observations = env.reset()
 
 while env.agents:
     # this is where you would insert your policy
-    actions = {agent: env.action_space(agent).sample() for agent in env.agents}  
+    actions = {agent: env.action_space(agent).sample() for agent in env.agents}
 
     observations, rewards, terminations, truncations, infos = env.step(actions)
 env.close()
@@ -63,15 +63,14 @@ manual_policy = knights_archers_zombies_v10.ManualPolicy(env)
 for agent in env.agent_iter():
     clock.tick(env.metadata["render_fps"])
     observation, reward, termination, truncation, info = env.last()
-    
+
     if agent == manual_policy.agent:
         # get user input (controls are WASD and space)
         action = manual_policy(observation, agent)
     else:
         # this is where you would insert your policy (for non-player agents)
         action = env.action_space(agent).sample()
 
-    env.step(action) 
+    env.step(action)
 env.close()
 ```
-

docs/environments/classic.md

@@ -23,7 +23,7 @@ classic/tictactoe
     :file: classic/list.html
 ```
 
-Classic environments represent implementations of popular turn-based human games and are mostly competitive. 
+Classic environments represent implementations of popular turn-based human games and are mostly competitive.
 
 
 ### Installation
@@ -45,14 +45,14 @@ env.reset(seed=42)
 
 for agent in env.agent_iter():
     observation, reward, termination, truncation, info = env.last()
-    
+
     if termination or truncation:
         break
-    
+
     mask = observation["action_mask"]
     action = env.action_space(agent).sample(mask)  # this is where you would insert your policy
-        
-    env.step(action)  
+
+    env.step(action)
 env.close()
 ```
 

docs/environments/mpe.md

@@ -43,13 +43,13 @@ env = simple_tag_v3.env(render_mode='human')
 env.reset()
 for agent in env.agent_iter():
     observation, reward, termination, truncation, info = env.last()
-    
+
     if termination or truncation:
         action = None
     else:
         action = env.action_space(agent).sample() # this is where you would insert your policy
-        
-    env.step(action) 
+
+    env.step(action)
 env.close()
 ```
 

docs/environments/sisl.md

@@ -36,13 +36,13 @@ env = waterworld_v4.env(render_mode='human')
 env.reset()
 for agent in env.agent_iter():
     observation, reward, termination, truncation, info = env.last()
-    
+
     if termination or truncation:
         action = None
     else:
         action = env.action_space(agent).sample() # this is where you would insert your policy
-        
-    env.step(action) 
+
+    env.step(action)
 env.close()
 ```