Adaptive Bayesian Optimization for Reinforcement Learning Reward Shaping

Abstract

Reward shaping is a critical technique in reinforcement learning (RL) that aims to accelerate learning by providing the agent with informative rewards. However, designing effective reward shaping functions can be a challenging and often tedious process, requiring domain expertise and extensive manual tuning. This paper proposes an adaptive Bayesian optimization approach to automate the reward shaping process. The system iteratively explores the space of potential reward functions, leveraging the agent's performance as feedback to refine the search strategy. We demonstrate that this approach can learn optimal reward shaping functions, leading to significant improvements in learning speed and agent performance compared to traditional reward shaping methods. The core claim is that an adaptive Bayesian optimization framework can effectively automate the reward shaping process, offering a more robust and efficient solution for complex RL problems. The system utilizes a Gaussian Process (GP) surrogate model to approximate the reward function landscape and employs an acquisition function, such as Expected Improvement, to guide the exploration process. The approach is evaluated on a suite of benchmark RL environments, showcasing its effectiveness across diverse scenarios. This work contributes to the broader field of RL by providing a practical and automated method for reward shaping, potentially unlocking new possibilities for tackling challenging RL problems.