Extending Group Relative Policy Optimization to Continuous Control: A Theoretical Framework for Robotic Reinforcement Learning
This work addresses a gap in robotic reinforcement learning by extending GRPO to continuous control, but it is incremental as it builds on existing methods without demonstrated practical impact.
The paper tackles the problem of applying Group Relative Policy Optimization (GRPO) to continuous control for robotics, presenting a theoretical framework with trajectory-based clustering and state-aware advantage estimation, but provides no empirical results or concrete numbers.
Group Relative Policy Optimization (GRPO) has shown promise in discrete action spaces by eliminating value function dependencies through group-based advantage estimation. However, its application to continuous control remains unexplored, limiting its utility in robotics where continuous actions are essential. This paper presents a theoretical framework extending GRPO to continuous control environments, addressing challenges in high-dimensional action spaces, sparse rewards, and temporal dynamics. Our approach introduces trajectory-based policy clustering, state-aware advantage estimation, and regularized policy updates designed for robotic applications. We provide theoretical analysis of convergence properties and computational complexity, establishing a foundation for future empirical validation in robotic systems including locomotion and manipulation tasks.