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  1. ; ; ; ; ; (, Formal Methods for Industrial Critical Systems. FMICS 2022)
    Groote, J.F.; Huisman, M. (Ed.)
    Reinforcement learning is a successful explore-and-exploit approach, where a controller tries to learn how to navigate an unknown environment. The principle approach is for an intelligent agent to learn how to maximise expected rewards. But what happens if the objective refers to non-terminating systems? We can obviously not wait until an infinite amount of time has passed, assess the success, and update. But what can we do? This talk will tell. 
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  2. ; ; ; ; ; (, Formal Methods (FM 2021))
    Huisman, M.; Păsăreanu, C.; Zhan, N. (Ed.)
    We study the problem of finding optimal strategies in Markov decision processes with lexicographic ω-regular objectives, which are ordered collections of ordinary ω-regular objectives. The goal is to compute strategies that maximise the probability of satisfaction of the first 𝜔-regular objective; subject to that, the strategy should also maximise the probability of satisfaction of the second ω-regular objective; then the third and so forth. For instance, one may want to guarantee critical requirements first, functional ones second and only then focus on the non-functional ones. We show how to harness the classic off-the-shelf model-free reinforcement learning techniques to solve this problem and evaluate their performance on four case studies. 
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