AI Research
Efficient Bayes-Adaptive Reinforcement Learning with Temporal Logic Specifications
Bridging autonomous reinforcement learning with rigorous formal safety specifications for complex environments.
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{\"u}chi Automaton (LDBA)...
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ArXiv CS.LG
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ArXiv CS.LG
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This research bridges the gap between flexible reinforcement learning and the rigid safety requirements needed for real-world deployment. By integrating formal logic directly into the learning loop, agents can autonomously navigate complex environments without violating critical constraints.
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