Average-Reward Maximum Entropy Reinforcement Learning for Global Policy in Double Pendulum Tasks

-- This report presents our reinforcement learning-based approach for the swing-up and stabilisation tasks of the acrobot and pendubot, tailored specifcially to the updated guidelines of the 3rd AI Olympics at ICRA 2025. Building upon our previously developed A verage-Reward Entropy Advantage Policy Optimization (AR-EAPO) algorithm, we refined our solution to effectively address the new competition scenarios and evaluation metrics. Extensive simulations validate that our controller robustly manages these revised tasks, demonstrating adaptability and effectiveness within the updated framework. Building upon prior competitions at IJCAI 2023 [3] and IROS 2024 [4], the current edition places particular emphasis on global policy robustness, requiring solutions for reliable swing-up stabilisation tasks from arbitrary initial configurations under significantly increased external disturbances. The competition maintains its use of two different configurations: the acrobot, characterised by an inactive shoulder joint, and the pendubot, with an inactive elbow joint.