4 citations · 11 across the 6 of their papers we have counts for
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cs.LG2019
-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank
Kefan Dong, Jian Peng, Yining Wang +1
In this paper, we consider the problem of online learning of Markov decision processes (MDPs) with very large state spaces. Under the assumptions of realizable function approximati…
cs.LG2019
Exploration via Hindsight Goal Generation
Zhizhou Ren, Kefan Dong, Yuan Zhou +2
Goal-oriented reinforcement learning has recently been a practical framework for robotic manipulation tasks, in which an agent is required to reach a certain goal defined by a func…
cs.LG2019
Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP
Kefan Dong, Yuanhao Wang, Xiaoyu Chen +1
A fundamental question in reinforcement learning is whether model-free algorithms are sample efficient. Recently, Jin et al. \cite{jin2018q} proposed a Q-learning algorithm with UC…