most citedOnline Reinforcement Learning for Real-Time Exploration in Continuous State and Action Markov Decision Processes

4 citations · 4 across the 3 of their papers we have counts for

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cs.AI2016★ 4 cited

Online Reinforcement Learning for Real-Time Exploration in Continuous State and Action Markov Decision Processes

Ludovic Hofer, Hugo Gimbert

This paper presents a new method to learn online policies in continuous state, continuous action, model-free Markov decision processes, with two properties that are crucial for pra…