Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learning
Haozhe Ma Zhengding Luo Thanh Vinh Vo Kuankuan Sima Tze-Yun Leong
Reward shaping is a reinforcement learning technique that addresses the sparse-reward problem by providing frequent, informative feedback. We propose an efficient self-adaptive reward-shaping mechanism that uses success rates derived from historical experiences as shaped rewards. The success rates are sampled from Beta distributions, which evolve from uncertainty to reliability as data accumulates. Initially, shaped rewards are stochastic to encourage exploration, gradually becoming more certain to promote exploitation and maintain a natural balance between exploration and exploitation. We apply Kernel Density Estimation (KDE) with Random Fourier Features (RFF) to derive Beta distributions, providing a computationally efficient solution for continuous and high-dimensional state spaces. Our method, validated on tasks with extremely sparse rewards, improves sample efficiency and convergence stability over relevant baselines.
Reinforcement Learning, Reward Shaping, Thompson Sampling, Self-Adaptive Exploration-Exploitation Balance
We propose a self-adaptive and highly efficient reward shaping mechanism by incorporating historical success rates into shaped rewards to improve sample efficiency and convergence stability in sparse-reward reinforcement learning environments.