Provides minimal, single-file code examples of reinforcement learning algorithms ranging from basic policy and value iteration to deep methods including DQN, A2C, and PPO. Each algorithm is implemented in a standalone Python file with training on Grid World, CartPole, and Atari environments. Includes benchmark results for training on Apple Silicon using the PyTorch MPS backend.