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Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning

Lanqing Li, Hai Zhang, Xinyu Zhang, Shatong Zhu, Yang Yu, Junqiao Zhao, Pheng-Ann Heng

TL;DR

The paper addresses COMRL by unifying offline meta-RL methods under an information-theoretic objective $I(Z; M)$, revealing that FOCAL, CORRO, and CSRO correspond to upper bounds, lower bounds, and convex interpolations of this quantity via a causal decomposition into $I(Z; X_t|X_b)$ and $I(Z; X_b)$. It introduces UNICORN, with a supervised variant and a self-supervised variant, to optimize $I(Z; M)$ and demonstrates strong in-distribution and exceptional out-of-distribution generalization across MuJoCo and MetaWorld benchmarks, even under varying data quality and model architectures. The framework is shown to be model-agnostic and extendable to transformer-based backbones and model-based RL through world-models, offering a principled path toward offline foundation-model pretraining for decision making. Overall, UNICORN provides a solid theoretical foundation and practical algorithms for robust task representation learning in COMRL, with promising implications for scalable, generalizable offline decision-making systems.

Abstract

As a marriage between offline RL and meta-RL, the advent of offline meta-reinforcement learning (OMRL) has shown great promise in enabling RL agents to multi-task and quickly adapt while acquiring knowledge safely. Among which, context-based OMRL (COMRL) as a popular paradigm, aims to learn a universal policy conditioned on effective task representations. In this work, by examining several key milestones in the field of COMRL, we propose to integrate these seemingly independent methodologies into a unified framework. Most importantly, we show that the pre-existing COMRL algorithms are essentially optimizing the same mutual information objective between the task variable $M$ and its latent representation $Z$ by implementing various approximate bounds. Such theoretical insight offers ample design freedom for novel algorithms. As demonstrations, we propose a supervised and a self-supervised implementation of $I(Z; M)$, and empirically show that the corresponding optimization algorithms exhibit remarkable generalization across a broad spectrum of RL benchmarks, context shift scenarios, data qualities and deep learning architectures. This work lays the information theoretic foundation for COMRL methods, leading to a better understanding of task representation learning in the context of reinforcement learning. Given its generality, we envision our framework as a promising offline pre-training paradigm of foundation models for decision making.

Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning

TL;DR

The paper addresses COMRL by unifying offline meta-RL methods under an information-theoretic objective , revealing that FOCAL, CORRO, and CSRO correspond to upper bounds, lower bounds, and convex interpolations of this quantity via a causal decomposition into and . It introduces UNICORN, with a supervised variant and a self-supervised variant, to optimize and demonstrates strong in-distribution and exceptional out-of-distribution generalization across MuJoCo and MetaWorld benchmarks, even under varying data quality and model architectures. The framework is shown to be model-agnostic and extendable to transformer-based backbones and model-based RL through world-models, offering a principled path toward offline foundation-model pretraining for decision making. Overall, UNICORN provides a solid theoretical foundation and practical algorithms for robust task representation learning in COMRL, with promising implications for scalable, generalizable offline decision-making systems.

Abstract

As a marriage between offline RL and meta-RL, the advent of offline meta-reinforcement learning (OMRL) has shown great promise in enabling RL agents to multi-task and quickly adapt while acquiring knowledge safely. Among which, context-based OMRL (COMRL) as a popular paradigm, aims to learn a universal policy conditioned on effective task representations. In this work, by examining several key milestones in the field of COMRL, we propose to integrate these seemingly independent methodologies into a unified framework. Most importantly, we show that the pre-existing COMRL algorithms are essentially optimizing the same mutual information objective between the task variable and its latent representation by implementing various approximate bounds. Such theoretical insight offers ample design freedom for novel algorithms. As demonstrations, we propose a supervised and a self-supervised implementation of , and empirically show that the corresponding optimization algorithms exhibit remarkable generalization across a broad spectrum of RL benchmarks, context shift scenarios, data qualities and deep learning architectures. This work lays the information theoretic foundation for COMRL methods, leading to a better understanding of task representation learning in the context of reinforcement learning. Given its generality, we envision our framework as a promising offline pre-training paradigm of foundation models for decision making.
Paper Structure (24 sections, 3 theorems, 23 equations, 8 figures, 7 tables, 2 algorithms)

This paper contains 24 sections, 3 theorems, 23 equations, 8 figures, 7 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Method
  3. Preliminaries, Problem Statement and Related Work
  4. A Unified Information Theoretic Framework
  5. Instantiations of UNICORN
  6. Experiments
  7. Experimental Setup
  8. Few-Shot Generalization to In-Distribution Data
  9. Few-Shot Generalization to Out-of-Distribution Behavior Policies
  10. Influence of Data Quality
  11. Discussion
  12. Is UNICORN Model-Agnostic?
  13. Can UNICORN be Exploited for Model-Based Paradigms?
  14. Conclusion & Limitation
  15. Pseudo-Code
  16. ...and 9 more sections

Key Result

Theorem 2.3

Let $\equiv$ denote equality up to a constant, then holds up to a constant, where

Figures (8)

  • Figure 1: Context shift of COMRL in Ant-Dir. Left: Given a task $M^i$ specified by a goal direction (dashed line), the RL agent is trained on data generated by a variety of behavior policies trained on the same task $M^i$ (red). At test time, however, the context might be collected by behavior policies trained on different tasks $\{M^j\}$ (blue), causing a context shift of OOD behavior policies (\ref{['sec:ood_experiments']}). Middle: Against OOD context, UNICORN (red) is more robust than baselines such as FOCAL (green) in terms of navigating the Ant robot towards the right direction. Right: Besides behavior policy, the task distribution (e.g., goal positions in Ant) can induce significant context shift (\ref{['sec:task_ood']}), which is also a challenging scenario for COMRL models to generalize.
  • Figure 2: Graphical Models of COMRL.
  • Figure 3: Meta-learning procedure of UNICORN-SS. The supervised variant UNICORN-SUP simply replaces the decoder by a classifier $p_{\bm{\theta}}(M|\bm{z})$ and optimize a cross-entropy loss instead of $\mathcal{L}_{\textup{recon}}$ and $\mathcal{L}_{\textup{FOCAL}}$.
  • Figure 4: Testing returns of UNICORN against baselines on six benchmarks. Solid curves refer to the mean performance of trials over 6 random seeds, and the shaded areas characterize the standard deviation of these trials.
  • Figure 5: Testing returns for OOD tasks. The learning curves are averaged over 6 random seeds.
  • ...and 3 more figures

Theorems & Definitions (10)

  • Definition 2.1: Task Representation Learning
  • Definition 2.2: Causal Decomposition
  • Theorem 2.3: Central Theorem
  • proof
  • Theorem 2.4: Concentration bound for supervised UNICORN
  • proof
  • Lemma B.1
  • proof
  • proof
  • proof