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RLeXplore provides stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and rewarding impact-driven exploration (RIDE).
Code for the paper "Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning", Advances in Neural Information Processing Systems (NeurIPS) 2024
A reinforcement learning agent trained using Q-Learning to solve OpenAI Gym’s FrozenLake environment. The project demonstrates value-based learning, policy improvement, and exploration strategies in a slippery gridworld setting.