Gym (formerly OpenAI Gym) is a Python library that provided a universal API for developing and comparing reinforcement learning (RL) algorithms across a diverse collection of environments. While it was historically the standard for RL environments, the `gym` library is no longer actively maintained. All future development and support have transitioned to its successor, `gymnasium`, a drop-in replacement. The last major release of `gym` was version 0.26.2, released in October 2022, which introduced significant breaking API changes.
pip install gymVerified import paths — ran on the pinned version, not inferred.
This example demonstrates how to create a CartPole-v1 environment, reset it with a seed, take random actions, and handle the new 5-tuple return value from `step()` and 2-tuple from `reset()` in Gym 0.26.x+. The environment is rendered to a human-viewable window.
Migrate your code to use `gymnasium`. The API is largely a drop-in replacement with `import gymnasium as gym`, but review `gymnasium` migration guides for version-specific changes, especially if upgrading from older `gym` versions.
Update your `step()` calls to unpack 5 values. Use `terminated or truncated` where you previously used `done`.
Update your `reset()` calls to unpack 2 values: `observation, info = env.reset(...)`. Access additional information from the `info` dictionary.
Replace `env.seed(my_seed)` with `env.reset(seed=my_seed)` when initializing or restarting an episode.
Provide `render_mode` when creating the environment with `gym.make()`. The `env.render()` method should then be called without arguments if rendering is enabled.
Install the necessary environment extras, e.g., `pip install 'gym[atari]'` for Atari environments, or `pip install 'gym[mujoco]'` for MuJoCo environments. Use `pip install 'gym[all]'` for all extras, though this can be substantial.
Remove `env.seed(seed)` and pass the seed directly to `env.reset()`. Additionally, `reset()` now returns both an observation and an `info` dictionary. ```python # Old (pre-0.26.0) Gym code # env.seed(42) # observation = env.reset() # New (0.26.0+) Gym code observation, info = env.reset(seed=42) ```
Adjust the unpacking of the `env.step()` return values to accommodate the new `terminated` and `truncated` flags.
```python
# Old (pre-0.26.0) Gym code
# observation, reward, done, info = env.step(action)
# if done:
# New (0.26.0+) Gym code
observation, reward, terminated, truncated, info = env.step(action)
if terminated or truncated:
# Handle episode end
pass
```Install the `gym` library using pip. If using a virtual environment, ensure it's activated before installation. ```bash pip install gym ```
Ensure that the custom environment's registration code is imported or the package containing it is installed in 'editable' mode (`pip install -e .`). For built-in environments, verify the ID's exact spelling, including any versioning (e.g., 'CartPole-v1').
```python
# For custom environments, ensure the module registering it is imported
import my_custom_gym_envs # Assuming this module contains the gym.register() call
env = gym.make('MyCustomEnv-v0') # Use the exact registered ID
```Downgrade NumPy to a compatible version (e.g., `numpy==1.23.5`) or migrate to the `gymnasium` library, which is the actively maintained successor to `gym` and is compatible with newer NumPy versions. ```bash pip uninstall numpy pip install numpy==1.23.5 # Or, migrate to gymnasium pip install gymnasium # Then update your code to import gymnasium as gym and adapt to its API if necessary ```