Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning

• URL: http://arxiv.org/abs/2009.13891v3
• Date: Tue, 15 Dec 2020 08:48:23 GMT
• Title: Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning
• Authors: Haotian Fu, Hongyao Tang, Jianye Hao, Chen Chen, Xidong Feng, Dong Li, Wulong Liu
• Abstract summary: We propose a novel Meta-RL framework called CCM (Contrastive learning augmented Context-based Meta-RL) We first focus on the contrastive nature behind different tasks and leverage it to train a compact and sufficient context encoder. We derive a new information-gain-based objective which aims to collect informative trajectories in a few steps.
• Score: 33.19862944149082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Context, the embedding of previous collected trajectories, is a powerful construct for Meta-Reinforcement Learning (Meta-RL) algorithms. By conditioning on an effective context, Meta-RL policies can easily generalize to new tasks within a few adaptation steps. We argue that improving the quality of context involves answering two questions: 1. How to train a compact and sufficient encoder that can embed the task-specific information contained in prior trajectories? 2. How to collect informative trajectories of which the corresponding context reflects the specification of tasks? To this end, we propose a novel Meta-RL framework called CCM (Contrastive learning augmented Context-based Meta-RL). We first focus on the contrastive nature behind different tasks and leverage it to train a compact and sufficient context encoder. Further, we train a separate exploration policy and theoretically derive a new information-gain-based objective which aims to collect informative trajectories in a few steps. Empirically, we evaluate our approaches on common benchmarks as well as several complex sparse-reward environments. The experimental results show that CCM outperforms state-of-the-art algorithms by addressing previously mentioned problems respectively.

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