Pareto Q-Learning with Reward Machines
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Computer Science > Machine Learning
Title:Pareto Q-Learning with Reward Machines
Abstract:We present Pareto Q-Learning with Reward Machines (PQLRM), a multi-objective reinforcement learning algorithm for tasks whose reward structure is specified by a set of reward machines (RMs). PQLRM combines Pareto Q-Learning (PQL), which maintains sets of vector-valued Q-estimates to approximate the Pareto front, with enhancements from Q-Learning with Reward Machines (QRM), which exploits the factored automaton structure of the reward signal. This yields a multi-policy algorithm that remains sample-efficient under non-Markovian, RM-encoded rewards. Experimental trials show that PQLRM converges faster than a naive PQL baseline applied to the cross-product MDP and can synthesize Pareto-optimal policies that QRM cannot.
| Comments: | Accepted at the ICAPS 2026 Workshop on Bridging the Gap Between AI Planning and (Reinforcement) Learning (PRL) |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.19134 [cs.LG] |
| (or arXiv:2606.19134v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.19134
arXiv-issued DOI via DataCite (pending registration)
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