Observations Meet Actions: Learning Control-Sufficient Representations for Robust Policy Generalization

2025-07-25

AI Summary

This work addresses robust out-of-distribution generalization of policies in reinforcement learning, focusing on learning representations with **control sufficiency**—not merely observational sufficiency. To this end, we formulate contextual RL as a **decoupled inference–control problem**, theoretically characterize the hierarchical relationship between observational and control sufficiency, and design an ELBO-style objective based on the variational information bottleneck to explicitly separate representation learning from policy optimization. Our method employs a variational encoder jointly with an off-policy policy learner. Evaluated on continuous-control benchmarks with physical parameter shifts, it achieves significantly improved sample efficiency and superior policy robustness under out-of-distribution dynamics compared to baselines. Moreover, it unifies theoretical analysis and practical implementation for contextual RL.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Capturing latent variations ("contexts") is key to deploying reinforcement-learning (RL) agents beyond their training regime. We recast context-based RL as a dual inference-control problem and formally characterize two properties and their hierarchy: observation sufficiency (preserving all predictive information) and control sufficiency (retaining decision-making relevant information). Exploiting this dichotomy, we derive a contextual evidence lower bound(ELBO)-style objective that cleanly separates representation learning from policy learning and optimizes it with Bottlenecked Contextual Policy Optimization (BCPO), an algorithm that places a variational information-bottleneck encoder in front of any off-policy policy learner. On standard continuous-control benchmarks with shifting physical parameters, BCPO matches or surpasses other baselines while using fewer samples and retaining performance far outside the training regime. The framework unifies theory, diagnostics, and practice for context-based RL.

Problem

• Learning control-sufficient representations for robust policy generalization
• Addressing context-based RL as dual inference-control problem
• Optimizing representation and policy learning with BCPO algorithm

Innovation

• Dual inference-control problem reformulation
• Variational information-bottleneck encoder integration
• Contextual ELBO objective separation