arXiv:2601.21924v2 Announce Type: replace Abstract: We study online transfer reinforcement learning (RL) in episodic Markov decision processes, where experience from related source tasks is available during learning on a target task. A fundamental difficulty is that task similarity is typically defined in terms of rewards or transitions, whereas online RL algorithms operate on Bellman regression targets. As a result, naively reusing source Bellman updates introduces systematic bias and invalidates regret guarantees. We identify one-step Bellman alignment as the correct abstraction for transfer
This research addresses a fundamental challenge in online RL where existing methods struggle with transferring knowledge between tasks due to differing reward and transition structures, which currently limits the broader application of RL.
For a strategic reader, this research introduces a method that could significantly improve the efficiency and robustness of online reinforcement learning, potentially accelerating the development and deployment of more adaptable AI systems.
The identification and application of 'one-step Bellman alignment' as a transfer mechanism allows for more principled and provably efficient transfer learning in online RL, reducing prior limitations of systematic bias.
- · AI researchers
- · Developers of AI agents
- · Sectors adopting reinforcement learning for complex tasks
- · Developers of less efficient, biased transfer learning methods
This method could lead to faster training times and more effective knowledge reuse in online reinforcement learning applications.
Improved transfer learning accelerates the development of more general-purpose AI agents capable of adapting to new environments quickly.
This could contribute to the broader commercialization and economic impact of AI systems that can learn and adapt on the fly.
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Read at arXiv cs.LG