Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning
AbstractContext, 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.
How to Cite
Fu, H., Tang, H., Hao, J., Chen, C., Feng, X., Li, D., & Liu, W. (2021). Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 7457-7465. https://doi.org/10.1609/aaai.v35i8.16914
AAAI Technical Track on Machine Learning I