most citedModel-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes

19 citations · 19 across the 1 of their papers we have counts for

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2 papers

cs.LG2020

Randomized Policy Learning for Continuous State and Action MDPs

Hiteshi Sharma, Rahul Jain

Deep reinforcement learning methods have achieved state-of-the-art results in a variety of challenging, high-dimensional domains ranging from video games to locomotion. The key to…

cs.LG2019★ 19 cited

Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes

Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo +2

Model-free reinforcement learning is known to be memory and computation efficient and more amendable to large scale problems. In this paper, two model-free algorithms are introduce…