Rename to gymnasium

.github/workflows/build.yml

@@ -14,4 +14,4 @@ jobs:
              --build-arg PYTHON_VERSION=${{ matrix.python-version }} \
              --tag gym-docker .
       - name: Run tests
-        run: docker run gym-docker pytest
+        run: docker run gymnasium-docker pytest

.pre-commit-config.yaml

@@ -15,7 +15,7 @@ repos:
     hooks:
       - id: flake8
         args:
-          - '--per-file-ignores=*/__init__.py:F401 gym/envs/registration.py:E704'
+          - '--per-file-ignores=*/__init__.py:F401 gymnasium/envs/registration.py:E704'
           - --ignore=E203,W503,E741
           - --max-complexity=30
           - --max-line-length=456
@@ -30,7 +30,7 @@ repos:
     rev: 6.1.1  # pick a git hash / tag to point to
     hooks:
       - id: pydocstyle
-        exclude: ^(gym/version.py)|(gym/envs/)|(tests/)
+        exclude: ^(gymnasium/version.py)|(gymnasium/envs/)|(tests/)
         args:
           - --source
           - --explain

CONTRIBUTING.md

@@ -1,21 +1,15 @@
-# Gym Contribution Guidelines
+# Gymnasium Contribution Guidelines
 
 At this time we are currently accepting the current forms of contributions:
 
 - Bug reports (keep in mind that changing environment behavior should be minimized as that requires releasing a new version of the environment and makes results hard to compare across versions)
 - Pull requests for bug fixes
 - Documentation improvements
+- Features
 
 Notably, we are not accepting these forms of contributions:
 
 - New environments
-- New features
-
-This may change in the future.
-If you wish to make a Gym environment, follow the instructions in [Creating Environments](https://github.com/openai/gym/blob/master/docs/creating_environments.md).  When your environment works, you can make a PR to add it to the bottom of the [List of Environments](https://github.com/openai/gym/blob/master/docs/third_party_environments.md).
-
-
-Edit July 27, 2021: Please see https://github.com/openai/gym/issues/2259 for new contributing standards
 
 # Development
 This section contains technical instructions & hints for the contributors.

README.md

@@ -25,13 +25,13 @@ We support Python 3.7, 3.8, 3.9 and 3.10 on Linux and macOS. We will accept PRs
 The Gym API's API models environments as simple Python `env` classes. Creating environment instances and interacting with them is very simple- here's an example using the "CartPole-v1" environment:
 
 ```python
-import gym
+import gymnasium as gym
 env = gym.make("CartPole-v1")
 observation, info = env.reset(seed=42)
 
 for _ in range(1000):
     action = env.action_space.sample()
-    observation, reward, terminated, truncarted, info = env.step(action)
+    observation, reward, terminated, truncated, info = env.step(action)
 
     if terminated or truncated:
         observation, info = env.reset()
@@ -43,7 +43,7 @@ env.close()
 * [Stable Baselines 3](https://github.com/DLR-RM/stable-baselines3) is a learning library based on the Gym API. It is designed to cater to complete beginners in the field who want to start learning things quickly.
 * [RL Baselines3 Zoo](https://github.com/DLR-RM/rl-baselines3-zoo) builds upon SB3, containing optimal hyperparameters for Gym environments as well as code to easily find new ones.
 * [Tianshou](https://github.com/thu-ml/tianshou) is a learning library that's geared towards very experienced users and is design to allow for ease in complex algorithm modifications.
-* [RLlib](https://docs.ray.io/en/latest/rllib/index.html) is a learning library that allows for distributed training and inferencing and supports an extraordinarily large number of features throughout the reinforcement learning space.
+* [RLlib](https://docs.ray.io/en/latest/rllib/index.html) is a learning library that allows for distributed training and inference and supports an extraordinarily large number of features throughout the reinforcement learning space.
 * [PettingZoo](https://github.com/Farama-Foundation/PettingZoo) is like Gym, but for environments with multiple agents.
 
 ## Environment Versioning
@@ -70,4 +70,4 @@ A whitepaper from when Gym just came out is available https://arxiv.org/pdf/1606
 
 ## Release Notes
 
-There used to be release notes for all the new Gym versions here. New release notes are being moved to [releases page](https://github.com/openai/gym/releases) on GitHub, like most other libraries do. Old notes can be viewed [here](https://github.com/openai/gym/blob/31be35ecd460f670f0c4b653a14c9996b7facc6c/README.rst).
+There used to be release notes for all the new Gym versions here. New release notes are being moved to [releases page](https://github.com/Farama-Foundation/Gymnasium/releases) on GitHub, like most other libraries do.

gym/__init__.py → gymnasium/__init__.py

@@ -1,21 +1,21 @@
-"""Root __init__ of the gym module setting the __all__ of gym modules."""
+"""Root __init__ of the gymnasium module setting the __all__ of gymnasium modules."""
 # isort: skip_file
 
-from gym import error
-from gym.version import VERSION as __version__
+from gymnasium import error
+from gymnasium.version import VERSION as __version__
 
-from gym.core import (
+from gymnasium.core import (
     Env,
     Wrapper,
     ObservationWrapper,
     ActionWrapper,
     RewardWrapper,
 )
-from gym.spaces import Space
-from gym.envs import make, spec, register
-from gym import logger
-from gym import vector
-from gym import wrappers
+from gymnasium.spaces import Space
+from gymnasium.envs import make, spec, register
+from gymnasium import logger
+from gymnasium import vector
+from gymnasium import wrappers
 import os
 import sys
 

gym/core.py → gymnasium/core.py

@@ -15,12 +15,12 @@
 
 import numpy as np
 
-from gym import spaces
-from gym.logger import warn
-from gym.utils import seeding
+from gymnasium import spaces
+from gymnasium.logger import warn
+from gymnasium.utils import seeding
 
 if TYPE_CHECKING:
-    from gym.envs.registration import EnvSpec
+    from gymnasium.envs.registration import EnvSpec
 
 if sys.version_info[0:2] == (3, 6):
     warn(
@@ -51,7 +51,7 @@ class Env(Generic[ObsType, ActType]):
     - :attr:`action_space` - The Space object corresponding to valid actions
     - :attr:`observation_space` - The Space object corresponding to valid observations
     - :attr:`reward_range` - A tuple corresponding to the minimum and maximum possible rewards
-    - :attr:`spec` - An environment spec that contains the information used to initialise the environment from `gym.make`
+    - :attr:`spec` - An environment spec that contains the information used to initialise the environment from `gymnasium.make`
     - :attr:`metadata` - The metadata of the environment, i.e. render modes
     - :attr:`np_random` - The random number generator for the environment
 
@@ -188,7 +188,7 @@ def unwrapped(self) -> "Env":
         """Returns the base non-wrapped environment.
 
         Returns:
-            Env: The base non-wrapped gym.Env instance
+            Env: The base non-wrapped gymnasium.Env instance
         """
         return self
 
@@ -362,7 +362,7 @@ class ObservationWrapper(Wrapper):
     ``observation["target_position"] - observation["agent_position"]``. For this, you could implement an
     observation wrapper like this::
 
-        class RelativePosition(gym.ObservationWrapper):
+        class RelativePosition(gymnasium.ObservationWrapper):
             def __init__(self, env):
                 super().__init__(env)
                 self.observation_space = Box(shape=(2,), low=-np.inf, high=np.inf)
@@ -402,7 +402,7 @@ class RewardWrapper(Wrapper):
     because it is intrinsic), we want to clip the reward to a range to gain some numerical stability.
     To do that, we could, for instance, implement the following wrapper::
 
-        class ClipReward(gym.RewardWrapper):
+        class ClipReward(gymnasium.RewardWrapper):
             def __init__(self, env, min_reward, max_reward):
                 super().__init__(env)
                 self.min_reward = min_reward
@@ -433,10 +433,10 @@ class ActionWrapper(Wrapper):
     In that case, you need to specify the new action space of the wrapper by setting :attr:`self.action_space` in
     the :meth:`__init__` method of your wrapper.
 
-    Let’s say you have an environment with action space of type :class:`gym.spaces.Box`, but you would only like
+    Let’s say you have an environment with action space of type :class:`gymnasium.spaces.Box`, but you would only like
     to use a finite subset of actions. Then, you might want to implement the following wrapper::
 
-        class DiscreteActions(gym.ActionWrapper):
+        class DiscreteActions(gymnasium.ActionWrapper):
             def __init__(self, env, disc_to_cont):
                 super().__init__(env)
                 self.disc_to_cont = disc_to_cont
@@ -446,7 +446,7 @@ def action(self, act):
                 return self.disc_to_cont[act]
 
         if __name__ == "__main__":
-            env = gym.make("LunarLanderContinuous-v2")
+            env = gymnasium.make("LunarLanderContinuous-v2")
             wrapped_env = DiscreteActions(env, [np.array([1,0]), np.array([-1,0]),
                                                 np.array([0,1]), np.array([0,-1])])
             print(wrapped_env.action_space)         #Discrete(4)