Offline meta reinforcement learning (OMRL) has emerged as a promising approach for interaction avoidance and strong generalization performance by leveraging pre-collected data and meta-learning techniques. Previous context-based approaches predominantly rely on the intuition that alternating optimization between the context encoder and the policy can lead to performance improvements, as long as the context encoder follows the principle of maximizing the mutual information between the task variable and its latent representation () while the policy adopts the standard offline reinforcement learning (RL) algorithms conditioning on the learned taskthis http URLpromising results, the theoretical justification of performance improvements for such intuition remainsthis http URLby the return discrepancy scheme in the model-based RL field, we find that the previous optimization framework can be linked with the general RL objective of maximizing the expected return, thereby explaining performance improvements. Furthermore, after scrutinizing this optimization framework, we observe that the condition for monotonic performance improvements does not consider the variation of the task representation. When these variations are considered, the previously established condition may no longer be sufficient to ensure monotonicity, thereby impairing the optimizationthis http URLname this issue task representation shift and theoretically prove that the monotonic performance improvements can be guaranteed with appropriate context encoderthis http URLwork opens up a new avenue for OMRL, leading to a better understanding between the task representation and performance improvements.
View on arXiv@article{zhang2025_2405.12001,
title={ Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement Learning },
author={ Hai Zhang and Boyuan Zheng and Tianying Ji and Jinhang Liu and Anqi Guo and Junqiao Zhao and Lanqing Li },
journal={arXiv preprint arXiv:2405.12001},
year={ 2025 }
}