Web: http://arxiv.org/abs/2206.11708

June 24, 2022, 1:10 a.m. | Edi Muskardin, Martin Tappler, Bernhard K. Aichernig, Ingo Pill

cs.LG updates on arXiv.org arxiv.org

In practical applications, we can rarely assume full observability of a
system's environment, despite such knowledge being important for determining a
reactive control system's precise interaction with its environment. Therefore,
we propose an approach for reinforcement learning (RL) in partially observable
environments. While assuming that the environment behaves like a partially
observable Markov decision process with known discrete actions, we assume no
knowledge about its structure or transition probabilities.

Our approach combines Q-learning with IoAlergia, a method for learning Markov …

arxiv environment learning lg models observability reinforcement reinforcement learning

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