Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning

• URL: http://arxiv.org/abs/2306.08388v3
• Date: Fri, 12 Jul 2024 01:59:00 GMT
• Title: Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning
• Authors: Ce Hao, Catherine Weaver, Chen Tang, Kenta Kawamoto, Masayoshi Tomizuka, Wei Zhan,
• Abstract summary: We propose the Skill-Critic algorithm to fine-tune the low-level policy in conjunction with high-level skill selection. We validate Skill-Critic in multiple sparse-reward environments, including a new sparse-reward autonomous racing task in Gran Turismo Sport.
• Score: 48.75878234995544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Code and videos are available at our website: https://sites.google.com/view/skill-critic.

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