10.1 Introduction RL Problems

Created Date: 2025-06-27

Gymnasium is an open source Python library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API.

Gymnasium includes the following families of environments along with a wide variety of third-party environments:

• Classic Control - These are classic reinforcement learning based on real-world problems and physics.

• Box2D - These environments all involve toy games based around physics control, using box2d based physics and PyGame-based rendering.

• Toy Text - These environments are designed to be extremely simple, with small discrete state and action spaces, and hence easy to learn. As a result, they are suitable for debugging implementations of reinforcement learning algorithms.

• MuJoCo - A physics engine based environments with multi-joint control which are more complex than the Box2D environments.

• Atari - Emulator of Atari 2600 ROMs simulated that have a high range of complexity for agents to learn.

• Third-party - A number of environments have been created that are compatible with the Gymnasium API. Be aware of the version that the software was created for and use the apply_env_compatibility in gymnasium.make if necessary.