バリア関数に着想を得た強化学習のための報酬整形
Barrier Functions Inspired Reward Shaping for Reinforcement Learning
バリア関数を応用した安全志向の報酬整形フレームワークを提案し、シミュレーションと実機ロボットで学習の高速化と省エネルギー化を実現した。
著者: Nilaksh Nilaksh, Abhishek Ranjan, Shreenabh Agrawal, Aayush Jain, Pushpak Jagtap, Shishir Kolathaya
分類: cs.RO
原文アブストラクト
Reinforcement Learning (RL) has progressed from simple control tasks to complex real-world challenges with large state spaces. While RL excels in these tasks, training time remains a limitation. Reward shaping is a popular solution, but existing methods often rely on value functions, which face scalability issues. This paper presents a novel safety-oriented reward-shaping framework inspired by barrier functions, offering simplicity and ease of implementation across various environments and tasks. To evaluate the effectiveness of the proposed reward formulations, we conduct simulation experiments on CartPole, Ant, and Humanoid environments, along with real-world deployment on the Unitree Go1 quadruped robot. Our results demonstrate that our method leads to 1.4-2.8 times faster convergence and as low as 50-60% actuation effort compared to the vanilla reward. In a sim-to-real experiment with the Go1 robot, we demonstrated better control and dynamics of the bot with our reward framework.
