  Generalization and sample efficiency have been long-standing issues
concerning reinforcement learning, and thus the field of Offline
Meta-Reinforcement Learning~(OMRL) has gained increasing attention due to its
potential of solving a wide range of problems with static and limited offline
data. Existing OMRL methods often assume sufficient training tasks and data
coverage to apply contrastive learning to extract task representations.
However, such assumptions are not applicable in several real-world applications
and thus undermine the generalization ability of the representations. In this
paper, we consider OMRL with two types of data limitations: limited training
tasks and limited behavior diversity and propose a novel algorithm called
GENTLE for learning generalizable task representations in the face of data
limitations. GENTLE employs Task Auto-Encoder~(TAE), which is an
encoder-decoder architecture to extract the characteristics of the tasks.
Unlike existing methods, TAE is optimized solely by reconstruction of the state
transition and reward, which captures the generative structure of the task
models and produces generalizable representations when training tasks are
limited. To alleviate the effect of limited behavior diversity, we consistently
construct pseudo-transitions to align the data distribution used to train TAE
with the data distribution encountered during testing. Empirically, GENTLE
significantly outperforms existing OMRL methods on both in-distribution tasks
and out-of-distribution tasks across both the given-context protocol and the
one-shot protocol.
