arXiv:2604.15414v2 Announce Type: replace Abstract: Continual reinforcement learning must balance retention with adaptation, yet many methods still rely on \emph{single-model preservation}, committing to one evolving policy as the main reusable solution across tasks. Even when a previously successful policy is retained, it may no longer provide a reliable starting point for rapid adaptation after interference, reflecting a form of \emph{loss of plasticity} that single-policy preservation cannot address. Inspired by quality-diversity methods, we introduce \textsc{TeLAPA} (Transfer-Enabled Laten
The paper addresses a critical limitation in continual reinforcement learning, a field gaining prominence as AI systems move towards more dynamic and adaptive applications.
Improving the ability of AI models to continually learn without forgetting past knowledge (plasticity) is crucial for real-world deployment across various sectors, enabling more robust and adaptable autonomous systems.
This research introduces a new approach, TeLAPA, that moves beyond single-model optimization, potentially leading to more resilient and efficient continual learning algorithms in AI, especially for complex, multi-task environments.
- · AI research & development
- · Robotics industry
- · Autonomous systems developers
- · Continual learning applications
- · Developers reliant solely on single-model continual learning
- · Systems with high catastrophic forgetting rates
- · Static AI model approaches
More capable and adaptable AI agents emerge, better handling new tasks without degrading prior skills.
Accelerated development of AI systems for dynamic environments such as autonomous vehicles or complex industrial control.
Reduced update costs and increased operational lifespan for intelligent systems, improving economic viability and deployment scalability.
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Read at arXiv cs.LG