Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning

• Published in: IEEE robotics and automation letters 2024-04, Vol.9 (4), p.3625-3632
• Main Authors: Hao, Ce, Weaver, Catherine, Tang, Chen, Kawamoto, Kenta, Tomizuka, Masayoshi, Zhan, Wei
• Format: Article
• Language: English
• Subjects: Algorithms; Decoding; Optimization; Policies; Regularization; Reinforcement learning; representation learning; Skills; Sports; Task analysis; Training; transfer learning
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2024.3368231

Hierarchical reinforcement learning (RL) can accelerate long-horizon decision-making by temporally abstracting a policy into multiple levels. Promising results in sparse reward environments have been seen with skills , i.e. sequences of primitive actions. Typically, a skill latent space and policy are discovered from offline data. However, the resulting low-level policy can be unreliable due to low-coverage demonstrations or distribution shifts. As a solution, we propose the Skill-Critic algorithm to fine-tune the low-level policy in conjunction with high-level skill selection. Our Skill-Critic algorithm optimizes both the low-level and high-level policies; these policies are initialized and regularized by the latent space learned from offline demonstrations to guide the parallel policy optimization. We validate Skill-Critic in multiple sparse-reward RL environments, including a new sparse-reward autonomous racing task in Gran Turismo Sport. The experiments show that Skill-Critic's low-level policy fine-tuning and demonstration-guided regularization are essential for good performance.