Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning
This work addresses the challenge of effective task generalization in Meta-RL, which is incremental as it builds on existing context-based methods by enhancing context quality.
The paper tackled the problem of improving context quality in Meta-Reinforcement Learning by proposing a framework that uses contrastive learning for better context encoding and an information-gain objective for collecting informative trajectories, resulting in outperforming state-of-the-art algorithms on benchmarks and complex sparse-reward environments.
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.