Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient

25 Jul 2020 Haonan Jia Xiao Zhang Jun Xu Wei Zeng Hao Jiang Xiaohui Yan Ji-Rong Wen

Deep Q-learning algorithms often suffer from poor gradient estimations with an excessive variance, resulting in unstable training and poor sampling efficiency. Stochastic variance-reduced gradient methods such as SVRG have been applied to reduce the estimation variance (Zhao et al. 2019)... (read more)

PDF Abstract
No code implementations yet. Submit your code now

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper