rl-agents
This project provides a diverse set of reinforcement learning agents specializing in planning, safe exploration, and value-based strategies. Featuring implementations like Value Iteration, Monte-Carlo Tree Search, and Deep Q-Networks, it supports both deterministic and stochastic environments and integrates seamlessly with OpenAI's Gym. Equipped with monitoring tools such as Gym Monitor and Tensorboard, this toolkit facilitates efficient experimentation with various configurations, offering a valuable resource for AI research and development.
