  Offline meta-reinforcement learning (OMRL) proficiently allows an agent to
tackle novel tasks while solely relying on a static dataset. For precise and
efficient task identification, existing OMRL research suggests learning
separate task representations that be incorporated with policy input, thus
forming a context-based meta-policy. A major approach to train task
representations is to adopt contrastive learning using multi-task offline data.
The dataset typically encompasses interactions from various policies (i.e., the
behavior policies), thus providing a plethora of contextual information
regarding different tasks. Nonetheless, amassing data from a substantial number
of policies is not only impractical but also often unattainable in realistic
settings. Instead, we resort to a more constrained yet practical scenario,
where multi-task data collection occurs with a limited number of policies. We
observed that learned task representations from previous OMRL methods tend to
correlate spuriously with the behavior policy instead of reflecting the
essential characteristics of the task, resulting in unfavorable
out-of-distribution generalization. To alleviate this issue, we introduce a
novel algorithm to disentangle the impact of behavior policy from task
representation learning through a process called adversarial data augmentation.
Specifically, the objective of adversarial data augmentation is not merely to
generate data analogous to offline data distribution; instead, it aims to
create adversarial examples designed to confound learned task representations
and lead to incorrect task identification. Our experiments show that learning
from such adversarial samples significantly enhances the robustness and
effectiveness of the task identification process and realizes satisfactory
out-of-distribution generalization.
