Web: http://arxiv.org/abs/2206.11430

June 24, 2022, 1:10 a.m. | Ernst Moritz Hahn, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi, Dominik Wojtczak

cs.LG updates on arXiv.org arxiv.org

Recursion is the fundamental paradigm to finitely describe potentially
infinite objects. As state-of-the-art reinforcement learning (RL) algorithms
cannot directly reason about recursion, they must rely on the practitioner's
ingenuity in designing a suitable "flat" representation of the environment. The
resulting manual feature constructions and approximations are cumbersome and
error-prone; their lack of transparency hampers scalability. To overcome these
challenges, we develop RL algorithms capable of computing optimal policies in
environments described as a collection of Markov decision processes (MDPs) that …

arxiv learning lg recursive reinforcement reinforcement learning

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