  Meta-learning for offline reinforcement learning (OMRL) is an understudied
problem with tremendous potential impact by enabling RL algorithms in many
real-world applications. A popular solution to the problem is to infer task
identity as augmented state using a context-based encoder, for which efficient
learning of robust task representations remains an open challenge. In this
work, we provably improve upon one of the SOTA OMRL algorithms, FOCAL, by
incorporating intra-task attention mechanism and inter-task contrastive
learning objectives, to robustify task representation learning against sparse
reward and distribution shift. Theoretical analysis and experiments are
presented to demonstrate the superior performance and robustness of our
end-to-end and model-free framework compared to prior algorithms across
multiple meta-RL benchmarks.
