MASH: Cooperative-Heterogeneous Multi-Agent Reinforcement Learning for Single Humanoid Robot Locomotion
This work addresses locomotion efficiency for humanoid robots, offering a novel integration of MARL into single-robot control, though it is incremental in applying existing MARL concepts to a new context.
The paper tackled enhancing locomotion for a single humanoid robot by applying cooperative-heterogeneous multi-agent reinforcement learning, treating each limb as an independent agent, and demonstrated accelerated training convergence and improved whole-body cooperation compared to conventional single-agent methods.
This paper proposes a novel method to enhance locomotion for a single humanoid robot through cooperative-heterogeneous multi-agent deep reinforcement learning (MARL). While most existing methods typically employ single-agent reinforcement learning algorithms for a single humanoid robot or MARL algorithms for multi-robot system tasks, we propose a distinct paradigm: applying cooperative-heterogeneous MARL to optimize locomotion for a single humanoid robot. The proposed method, multi-agent reinforcement learning for single humanoid locomotion (MASH), treats each limb (legs and arms) as an independent agent that explores the robot's action space while sharing a global critic for cooperative learning. Experiments demonstrate that MASH accelerates training convergence and improves whole-body cooperation ability, outperforming conventional single-agent reinforcement learning methods. This work advances the integration of MARL into single-humanoid-robot control, offering new insights into efficient locomotion strategies.