Automated Technology for Verification and Analysis - 20th International Symposium, ATVA 2022, Virtual Event, October 25-28, 2022, Proceedings
Automated Technology for Verification and Analysis - 20th International Symposium, ATVA 2022, Virtual Event, October 25-28, 2022, Proceedings
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验证和分析自动化技术 - 第 20 届国际研讨会,ATVA 2022,虚拟活动,2022 年 10 月 25-28 日,会议记录
DOI:
10.1007/978-3-031-19992-9_19
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发表时间:
2022
期刊:
影响因子:
--
通讯作者:
Hahn E
中科院分区:
文献类型:
--
作者:
Hahn E
When omega-regular objectives were first proposed in model-free reinforcement learning (RL) for controlling MDPs, deterministic Rabin automata were used in an attempt to provide a direct translation from their transitions to scalar values. While these translations failed, it has turned out that it is possible to repair them by using good-for-MDPs (GFM) Büchi automata instead. These are nondeterministic Büchi automata with a restricted type of nondeterminism, albeit not as restricted as in good-for-games automata. Indeed, deterministic Rabin automata have a pretty straightforward translation to such GFM automata, which is bi-linear in the number of states and pairs. Interestingly, the same cannot be said for deterministic Streett automata: a translation to nondeterministic Rabin or Büchi automata comes at an exponential cost, even without requiring the target automaton to be good-for-MDPs. Do we have to pay more than that to obtain a good-for-MDPs automaton? The surprising answer is that we have to pay significantly less when we instead expand the good-for-MDPs property to alternating automata: like the nondeterministic GFM automata obtained from deterministic Rabin automata, the alternating good-for-MDPs automata we produce from deterministic Streett automata are bi-linear in the size of the deterministic automaton and its index. They can therefore be exponentially more succinct than the minimal nondeterministic Büchi automaton.