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Summary
This research paper investigates the effectiveness of reward centering, a technique that involves subtracting the average reward from observed rewards in reinforcement learning problems. The authors demonstrate that this simple method can significantly improve the performance of standard reinforcement learning algorithms, particularly when using discounted rewards and as the discount factor approaches one. They explain the underlying theory behind this improvement, showing how centering removes a state-independent constant term from value estimates, enabling the algorithm to focus on the relative differences between states and actions. The paper also examines the application of reward centering in both on-policy and off-policy settings, proposing a more sophisticated method for the off-policy case, and provides a case study using Q-learning with various function approximation methods. The authors conclude that reward centering is a general technique that can enhance data efficiency and robustness in various reinforcement learning algorithms, offering potential for future algorithms that adapt their discount rate over time.
原文链接：https://arxiv.org/abs/2405.09999
