OpenGym is a toolkit developed by OpenAI for developing and comparing reinforcement learning (RL) algorithms. It provides:

Key Features:

Environments: A collection of standardized environments (simulated tasks) ranging from simple games to complex robotics simulations (e.g., CartPole, Atari games, MuJoCo).

Unified Interface: A common API that allows developers to test RL algorithms across different environments with minimal code changes.

Monitoring Tools: Built-in support for logging, visualizing performance, and benchmarking agents.

Compatibility: Works with various deep learning libraries (TensorFlow, PyTorch, etc.).

Typical Use Cases:

Training and evaluating RL agents.

Benchmarking new algorithms against standard baselines.

Learning and experimentation in academic or hobbyist projects.