Recently regular decision processes have been pro- posed as a well-behaved form of non-Markov decision process. Regular decision processes are characterised by a transition function and a reward function that depend on the whole history, though regularly (as in regular languages). In practice both the transition and the reward functions can be seen as finite transducers. We study reinforcement learning in regular decision processes. Our main contribution is to show that a near-optimal policy can be PAC-learned in polynomial time in a set of parameters that describe the underlying decision process. We argue that the identified set of parameters is minimal and it reasonably captures the difficulty of a regular decision process.

Efficient PAC Reinforcement Learning in Regular Decision Processes / Ronca, Alessandro; De Giacomo, Giuseppe. - (2021). ((Intervento presentato al convegno Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021 tenutosi a Montreal, Canada [https://doi.org/10.24963/ijcai.2021/279].

Efficient PAC Reinforcement Learning in Regular Decision Processes

Alessandro Ronca;Giuseppe De Giacomo
2021

Abstract

Recently regular decision processes have been pro- posed as a well-behaved form of non-Markov decision process. Regular decision processes are characterised by a transition function and a reward function that depend on the whole history, though regularly (as in regular languages). In practice both the transition and the reward functions can be seen as finite transducers. We study reinforcement learning in regular decision processes. Our main contribution is to show that a near-optimal policy can be PAC-learned in polynomial time in a set of parameters that describe the underlying decision process. We argue that the identified set of parameters is minimal and it reasonably captures the difficulty of a regular decision process.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1575264
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