Pits and Orbs is an open-source reinforcement learning environment implemented in Python, offering a turn-based multi-agent grid-world where agents pursue objectives and face environmental hazards. Each agent must navigate a customizable grid, avoid randomly placed pits that penalize or terminate episodes, and collect orbs for positive rewards. The environment supports both competitive and cooperative modes, enabling researchers to explore varied learning scenarios. Its simple API integrates seamlessly with popular RL libraries like Stable Baselines or RLlib. Key features include adjustable grid dimensions, dynamic pit and orb distributions, configurable reward structures, and optional logging for training analysis.

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.