Efficient Bayes-Adaptive Reinforcement Learning with Temporal Logic Specifications
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Computer Science > Machine Learning
arXiv:2609.20954 (cs)
[Submitted on 17 Sep 2026]
Title:Efficient Bayes-Adaptive Reinforcement Learning with Temporal Logic Specifications
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Abstract:We present a novel end-to-end model-based Reinforcement Learning (RL) algorithm for efficient policy synthesis under given Linear Temporal Logic (LTL) specifications (e.g., safety or reachability) in unknown environments. To do so, a Limit-Deterministic B{ü}chi Automaton (LDBA) representation of the LTL task is synchronised with a Bayes-Adaptive Markov Decision Process (BAMDP) representation of the environment, which allows us to leverage an enhanced exploration-exploitation trade-off that is achieved via Bayesian RL, as opposed to traditional non-Bayesian approaches. We further propose a novel Bayes-Adaptive Monte-Carlo Planning (BAMCP) algorithm to allow for approximate Bayes-optimal strategy synthesis in the synchronised BAMDP construct. A range of finite- and infinite-horizon task experiments demonstrate the effectiveness of our approach in terms of both property satisfaction and sample efficiency, when compared to traditional model-free approaches. Additional ablation studies also successfully highlight the value of the novel BAMCP algorithm in comparison to classical BAMCP for LTL task satisfaction. Finally, we also showcase a successful application of our approach for \textit{cautious} RL, namely to reduce the number of task violations incurred during policy training.
| Comments: | ©~2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.20954 [cs.LG] |
| (or arXiv:2609.20954v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.20954
arXiv-issued DOI via DataCite (pending registration)
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