Don't Forget the Critic: Value-Based Data Rehearsal for Multi-Cyclic Continual Reinforcement Learning
arXiv:2605.22454v1 Announce Type: new Abstract: Data rehearsal has emerged as a leading approach for mitigating catastrophic forgetting in Continual Reinforcement Learning (CRL). However, existing work remains confined to policy gradient frameworks, regularizing only actors due to the performance degradation incurred by critic regularization. This actor-centric approach overlooks the potential of data rehearsal for value function approximation. Moreover, existing evaluations in CRL rarely consider multi-cyclic environments where task sequences repeat, a critical real-world scenario that exacer
The continuous evolution of AI research focuses on overcoming challenges like catastrophic forgetting in complex learning environments, driven by the increasing demand for robust and adaptable AI systems.
This research addresses a fundamental limitation in Continual Reinforcement Learning, potentially advancing the development of more stable and generalisable AI agents capable of learning in dynamic, real-world scenarios without losing past knowledge.
The proposed 'value-based data rehearsal' method suggests a more comprehensive approach to mitigating catastrophic forgetting by regularizing both actors and critics, potentially improving the performance and stability of CRL systems in multi-cyclic environments.
- · AI researchers
- · Reinforcement Learning applications
- · Companies developing autonomous systems
- · AI agent developers
- · AI systems prone to catastrophic forgetting
- · Traditional policy gradient frameworks for CRL
Improved performance and stability in continual learning for complex AI agents across various domains.
Accelerated development and deployment of robust autonomous AI systems in industrial and consumer applications.
Enhanced AI capabilities contributing to sophisticated AI agents that can rapidly adapt to novel and repeated tasks, potentially impacting multiple white-collar workflows.
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Read at arXiv cs.LG