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

2024-02-04

arXiv.org

AI Summary

To address the challenge of enabling robots to rapidly adapt to multiple tasks and efficiently acquire new skills under safety constraints, this paper proposes Contextual Offline Meta-Reinforcement Learning (COMRL), a unified framework. Methodologically, COMRL integrates variational inference, context-conditioned policy networks, and self-supervised representation learning. Its key theoretical contribution is the first formal establishment of COMRL’s information-theoretic foundation: we prove that COMRL fundamentally maximizes the mutual information (I(Z; M)) between task variables (M) and latent representations (Z). Leveraging this insight, we design both supervised and self-supervised mechanisms for optimizing (I(Z; M)), providing a general paradigm for task representation learning. Empirically, COMRL demonstrates strong generalization across diverse RL benchmarks, under context shift, data degradation, and varying network architectures. This work establishes a novel offline pretraining paradigm for decision-making foundation models.

Technology Category

• Machine Learning: Reinforcement Learning
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search

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
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production 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.

Problem

• Reinforcement Learning
• Meta-Learning
• Robotics Education

Innovation

• Offline-Meta Reinforcement Learning
• Task-aware Robot Understanding
• Unified Framework for Decision-making