Markov abstractions for PAC reinforcement learning in non-Markov decision processes
Our work aims at developing reinforcement learning algorithms that do not rely on the Markov assumption. We consider the class of Non-Markov Decision Processes where histories can be abstracted into a finite set of states while preserving the dynamics. We call it a Markov abstraction since it induce...
| Главные авторы: | , , |
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| Формат: | Conference item |
| Язык: | English |
| Опубликовано: |
International Joint Conferences on Artificial Intelligence
2022
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