  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 task representations remains an open challenge. In this work, we
improve upon one of the SOTA OMRL algorithms, FOCAL, by incorporating
intra-task attention mechanism and inter-task contrastive learning objectives
for more effective task inference and learning of control. Theoretical analysis
and experiments are presented to demonstrate the superior performance,
efficiency and robustness of our end-to-end and model free method compared to
prior algorithms across multiple meta-RL benchmarks.
