PyGame Learning Environment (PLE) is an open-source Python framework designed to simplify the development, testing, and benchmarking of reinforcement learning agents within custom game scenarios. It provides a collection of lightweight Pygame-based games with built-in support for agent observations, discrete and continuous action spaces, reward shaping, and environment rendering. PLE features an easy-to-use API compatible with OpenAI Gym wrappers, enabling seamless integration with popular RL libraries such as Stable Baselines and TensorForce. Researchers and developers can customize game parameters, implement new games, and leverage vectorized environments for accelerated training. With active community contributions and extensive documentation, PLE serves as a versatile platform for academic research, education, and real-world RL application prototyping.

simple_rl is a minimalistic Python library designed to streamline reinforcement learning research and education. It provides a consistent API for defining environments and agents, with built-in support for common RL paradigms including Q-learning, Monte Carlo methods, and dynamic programming algorithms like value and policy iteration. The framework includes sample environments such as GridWorld, MountainCar, and Multi-Armed Bandits, facilitating hands-on experimentation. Users can extend base classes to implement custom environments or agents, while utility functions handle logging, performance tracking, and policy evaluation. simple_rl's lightweight architecture and clear codebase make it ideal for rapid prototyping, teaching RL fundamentals, and benchmarking new algorithms in a reproducible, easy-to-understand environment.