Transition Transfer $Q$-Learning for Composite Markov Decision Processes

• URL: http://arxiv.org/abs/2502.00534v1
• Date: Sat, 01 Feb 2025 19:22:00 GMT
• Title: Transition Transfer $Q$-Learning for Composite Markov Decision Processes
• Authors: Jinhang Chai, Elynn Chen, Lin Yang,
• Abstract summary: We introduce a novel composite MDP framework where high-dimensional transition dynamics are modeled as the sum of a low-rank component representing shared structure. This relaxes the common assumption of purely low-rank transition models. We introduce UCB-TQL, designed for transfer RL scenarios where multiple tasks share core linear MDP dynamics but diverge along sparse dimensions.
• Score: 6.337133205762491
• License:
• Abstract: To bridge the gap between empirical success and theoretical understanding in transfer reinforcement learning (RL), we study a principled approach with provable performance guarantees. We introduce a novel composite MDP framework where high-dimensional transition dynamics are modeled as the sum of a low-rank component representing shared structure and a sparse component capturing task-specific variations. This relaxes the common assumption of purely low-rank transition models, allowing for more realistic scenarios where tasks share core dynamics but maintain individual variations. We introduce UCB-TQL (Upper Confidence Bound Transfer Q-Learning), designed for transfer RL scenarios where multiple tasks share core linear MDP dynamics but diverge along sparse dimensions. When applying UCB-TQL to a target task after training on a source task with sufficient trajectories, we achieve a regret bound of $\tilde{O}(\sqrt{eH^5N})$ that scales independently of the ambient dimension. Here, $N$ represents the number of trajectories in the target task, while $e$ quantifies the sparse differences between tasks. This result demonstrates substantial improvement over single task RL by effectively leveraging their structural similarities. Our theoretical analysis provides rigorous guarantees for how UCB-TQL simultaneously exploits shared dynamics while adapting to task-specific variations.

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