Researchers analyze multitask Q-learning for improved generalization in offline RL

Researchers have developed a multitask variant of fitted Q-iteration designed to improve generalization in offline reinforcement learning. This method jointly learns a shared representation and task-specific value functions by minimizing Bellman error on fixed datasets from related tasks. The analysis shows that pooling data across tasks enhances estimation accuracy, yielding a $1/\sqrt{nT}$ dependence on total samples, while also improving downstream task learning by reusing the learned representation. AI

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IMPACT Provides theoretical insights into how shared representations can improve generalization in multitask offline reinforcement learning.

RANK_REASON This is a research paper detailing theoretical analysis and guarantees for a specific reinforcement learning algorithm.

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• arXiv
• Fitted Q-iteration
• Offline Q-learning
• Bellman error
• Kausthubh Manda

• paper
• other