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cs.LG2019
Biased Estimates of Advantages over Path Ensembles
Lanxin Lei, Zhizhong Li, Dahua Lin
The estimation of advantage is crucial for a number of reinforcement learning algorithms, as it directly influences the choices of future paths. In this work, we propose a family o…
cs.LG2018★ 2 cited
NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning
Sirui Xie, Junning Huang, Lanxin Lei +4
Reinforcement learning agents need exploratory behaviors to escape from local optima. These behaviors may include both immediate dithering perturbation and temporally consistent ex…