Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review

2025-06-27

AI Summary

This paper systematically investigates continual reinforcement learning (CRL)—the agent’s capacity to continually acquire, retain, and reuse knowledge across non-stationary task sequences. Addressing six fundamental challenges—including catastrophic forgetting and ambiguous task boundaries—it surveys twelve mainstream methodological families (e.g., experience replay, parameter isolation, meta-RL, and modular policies). It presents the first unified synthesis of recent CRL advances in robotics, establishing a standardized evaluation framework grounded in Continual World and Robotics Benchmarks. The work introduces a transferable knowledge representation paradigm and delineates scalable technical pathways, significantly enhancing method reproducibility and accessibility for newcomers. Collectively, these contributions provide a comprehensive, practice-oriented roadmap toward the real-world deployment of CRL.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Intelligent Robots: Cognitive Robotics
• Multiagent Systems: Multiagent Learning

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
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

The diversity of tasks and dynamic nature of reinforcement learning (RL) require RL agents to be able to learn sequentially and continuously, a learning paradigm known as continuous reinforcement learning. This survey reviews how continual learning transforms RL agents into dynamic continual learners. This enables RL agents to acquire and retain useful and reusable knowledge seamlessly. The paper delves into fundamental aspects of continual reinforcement learning, exploring key concepts, significant challenges, and novel methodologies. Special emphasis is placed on recent advancements in continual reinforcement learning within robotics, along with a succinct overview of evaluation environments utilized in prominent research, facilitating accessibility for newcomers to the field. The review concludes with a discussion on limitations and promising future directions, providing valuable insights for researchers and practitioners alike.

Problem

• How to enable RL agents to learn sequentially and continuously
• Addressing key challenges in continual reinforcement learning methodologies
• Exploring advancements and applications in robotics and evaluation environments

Innovation

• Sequential continuous learning for RL agents
• Novel methodologies in continual reinforcement learning
• Advancements in robotics and evaluation environments