Rethinking the Foundations for Continual Reinforcement Learning

2025-04-10

AI Summary

Traditional reinforcement learning (RL) rests on four foundational assumptions—Markov decision process (MDP) formalization, optimality-driven policy design, sole reliance on cumulative reward as the evaluation metric, and isolated episodic environments—which fundamentally conflict with the core objectives of continual reinforcement learning (CRL), including long-term adaptability, generalization across task sequences, and robustness to non-stationarity. Method: Through conceptual analysis and paradigmatic reconstruction—without dependence on algorithmic implementation—the paper systematically proposes a novel four-dimensional theoretical framework for CRL. Contribution/Results: This framework replaces MDPs with dynamic non-Markovian modeling; substitutes static optimal policies with adaptive policy evolution; introduces a multi-dimensional evaluation suite (encompassing stability, plasticity, and memory retention); and advocates a continual interactive environment paradigm. The work establishes a paradigm-level foundation for CRL theory, benchmark design, and evaluation standards.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Game Theory and Economic Paradigms: Adversarial Learning
• Reasoning under Uncertainty: Sequential Decision Making

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: Metrics for user behavior and evaluating success

Abstract

Algorithms and approaches for continual reinforcement learning have gained increasing attention. Much of this early progress rests on the foundations and standard practices of traditional reinforcement learning, without questioning if they are well-suited to the challenges of continual learning agents. We suggest that many core foundations of traditional RL are, in fact, antithetical to the goals of continual reinforcement learning. We enumerate four such foundations: the Markov decision process formalism, a focus on optimal policies, the expected sum of rewards as the primary evaluation metric, and episodic benchmark environments that embrace the other three foundations. Shedding such sacredly held and taught concepts is not easy. They are self-reinforcing in that each foundation depends upon and holds up the others, making it hard to rethink each in isolation. We propose an alternative set of all four foundations that are better suited to the continual learning setting. We hope to spur on others in rethinking the traditional foundations, proposing and critiquing alternatives, and developing new algorithms and approaches enabled by better-suited foundations.

Problem

• Challenges traditional RL foundations for continual learning
• Critiques Markov process and optimal policy focus
• Proposes new foundations for continual RL

Innovation

• Proposing alternative foundations for continual RL
• Challenging traditional MDP formalism and metrics
• Redefining evaluation for continual learning agents