MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration

• URL: http://arxiv.org/abs/2006.08170v5
• Date: Fri, 12 Nov 2021 03:15:55 GMT
• Title: MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration
• Authors: Jin Zhang, Jianhao Wang, Hao Hu, Tong Chen, Yingfeng Chen, Changjie Fan and Chongjie Zhang
• Abstract summary: Experimental evaluation shows that our meta-RL method significantly outperforms state-of-the-art baselines on sparse-reward tasks. We model an exploration policy learning problem for meta-RL, which is separated from exploitation policy learning. We develop a new off-policy meta-RL framework, which efficiently learns separate context-aware exploration and exploitation policies.
• Score: 52.48362697163477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Meta reinforcement learning (meta-RL) extracts knowledge from previous tasks and achieves fast adaptation to new tasks. Despite recent progress, efficient exploration in meta-RL remains a key challenge in sparse-reward tasks, as it requires quickly finding informative task-relevant experiences in both meta-training and adaptation. To address this challenge, we explicitly model an exploration policy learning problem for meta-RL, which is separated from exploitation policy learning, and introduce a novel empowerment-driven exploration objective, which aims to maximize information gain for task identification. We derive a corresponding intrinsic reward and develop a new off-policy meta-RL framework, which efficiently learns separate context-aware exploration and exploitation policies by sharing the knowledge of task inference. Experimental evaluation shows that our meta-RL method significantly outperforms state-of-the-art baselines on various sparse-reward MuJoCo locomotion tasks and more complex sparse-reward Meta-World tasks.

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