Efficient Bayes-Adaptive Reinforcement Learning with Temporal Logic Specifications
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 st...
| Authors | Jonathan Hau, Alessandro Abate |
|---|---|
| Primary category | cs.LG |
| Published | 17 September 2026 |
| Last updated at source | 21 September 2026 |
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