GCR-RL: Gradient Control Reward Shaping for Reinforcement Learning

Abstract

Designing an optimal reward function is fundamental to achieving stability and efficiency in reinforcement learning (RL). This is particularly critical in robotics, where sparse rewards often provide insufficient guidance, necessitating the inclusion of auxiliary state information to facilitate meaningful exploration. This work introduces Gradient Control Rewards (GCR), an interpretable, control-inspired reward-design methodology for accelerating agent training by modulating the reward signal based on the temporal dynamics of system error. Inspired by classical control theory, GCR partitions the reward into three distinct components, state alignment, bias correction, and dynamic stability. These elements synergistically discourage the accumulation of error and excessive velocity toward objectives, facilitating the acquisition of a well-regulated action policy. GCR was evaluated across diverse environments, ranging from simple pendulum simulations to high-fidelity robotic scenarios and external physical validation. Experimental results demonstrate that GCR achieves competitive performance compared to both conventional reward functions and adaptive methods such as Bootstrapped Reward Shaping (BSRS). While alternative approaches exhibit performance degradation in stochastic, real-world-representative simulations, GCR maintains robustness and has been successfully validated in external physical environments. These findings suggest that GCR offers a practical and interpretable framework for deploying RL in control-oriented physical systems.