rlcode/reinforcement-learning is a collection of educational code examples for reinforcement learning algorithms.
The repository addresses the need for clear, minimal implementations of reinforcement learning techniques spanning from foundational methods to deep learning approaches. It organizes algorithms by problem domain—grid world environments for basic methods, CartPole for intermediate techniques, and Atari games for advanced deep reinforcement learning—with one focused Python file per algorithm to keep implementations readable and self-contained.
This resource suits developers learning reinforcement learning concepts or seeking reference implementations to understand how algorithms work in practice. The progression from policy iteration and value iteration through deep Q-networks, actor-critic methods, and policy gradient approaches makes it appropriate for educational purposes or as a starting point before moving to production frameworks. The repository includes benchmark results on standard environments, demonstrating that implementations achieve reasonable performance on Atari games like Breakout and harder exploration problems like Montezuma's Revenge.
The project maintains a straightforward structure with implementations in PyTorch, includes performance benchmarks with resource usage metrics, and provides links to detailed training reports. Pull requests and issue contributions are explicitly welcomed, indicating openness to community input on code quality and algorithm coverage.
