Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement Learning

2024-05-20

AI Summary

Offline meta-reinforcement learning (OMRL) suffers from non-monotonic performance optimization and degraded convergence due to task representation drift—a phenomenon wherein task embeddings shift unpredictably across training iterations, undermining optimization stability. Method: This work formally defines task representation drift and theoretically characterizes its detrimental impact on optimization monotonicity. We propose a unified analytical framework integrating mutual information maximization, reward discrepancy analysis, and task representation dynamics modeling. Based on this framework, we derive a verifiable context encoder update criterion that rigorously guarantees monotonic improvement of the expected return. Contribution/Results: Our criterion rectifies theoretical deficiencies in existing context optimization paradigms and establishes the first task representation learning principle for OMRL with provable monotonicity guarantees. It enhances both algorithmic stability and model interpretability without compromising empirical performance.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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 $M$ and its latent representation $Z$ ($I(Z;M)$) while the policy adopts the standard offline reinforcement learning (RL) algorithms conditioning on the learned task representation.Despite promising results, the theoretical justification of performance improvements for such intuition remains underexplored.Inspired by 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 optimization process.We name this issue task representation shift and theoretically prove that the monotonic performance improvements can be guaranteed with appropriate context encoder updates.Our work opens up a new avenue for OMRL, leading to a better understanding between the task representation and performance improvements.

Problem

• Offline Meta Reinforcement Learning
• Task Representation Drift
• Learning Stability

Innovation

• Offline Meta Reinforcement Learning
• Contextual Encoding Adjustment
• Theoretical Explanation