In meta reinforcement learning (meta RL), an agent seeks a Bayes-optimal
policy -- the optimal policy when facing an unknown task that is sampled from
some known task distribution. Previous approaches tackled this problem by
inferring a belief over task parameters, using variational inference methods.
Motivated by recent successes of contrastive learning approaches in RL, such as
contrastive predictive coding (CPC), we investigate whether contrastive methods
can be used for learning Bayes-optimal behavior. We begin by proving that
representations learned by CPC are indeed sufficient for Bayes optimality.
Based on this observation, we propose a simple meta RL algorithm that uses CPC
in lieu of variational belief inference. Our method, ContraBAR, achieves
comparable performance to state-of-the-art in domains with state-based
observation and circumvents the computational toll of future observation
reconstruction, enabling learning in domains with image-based observations. It
can also be combined with image augmentations for domain randomization and used
seamlessly in both online and offline meta RL settings.

本文提出了一种基于对比学习方法的元 RL 算法 ContraBAR，可以在状态观测及基于图像观测的领域中有效地学习 Bayes 最优行为，并可以与图像增强相结合，用于领域随机化，并可以无缝地应用于在线和离线元 RL 设置。