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Hasselt, H.V. (2010) Double Q-Learning. In: Lafferty, J.D., Williams, C.K.I., Shawe-Taylor, J., Zemel, R.S. and Culotta, A., Eds., Advances in Neural Information Processing Systems, Curran Associates, Inc., New York, 2613-2621.

has been cited by the following article:

• TITLE: Double Sarsa and Double Expected Sarsa with Shallow and Deep Learning

  AUTHORS: Michael Ganger, Ethan Duryea, Wei Hu

  KEYWORDS: Double Sarsa, Double Expected Sarsa, Reinforcement Learning, Deep Learning

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.4 No.4, October 17, 2016

  ABSTRACT: Double Q-learning has been shown to be effective in reinforcement learning scenarios when the reward system is stochastic. We apply the idea of double learning that this algorithm uses to Sarsa and Expected Sarsa, producing two new algorithms called Double Sarsa and Double Expected Sarsa that are shown to be more robust than their single counterparts when rewards are stochastic. We find that these algorithms add a significant amount of stability in the learning process at only a minor computational cost, which leads to higher returns when using an on-policy algorithm. We then use shallow and deep neural networks to approximate the actionvalue, and show that Double Sarsa and Double Expected Sarsa are much more stable after convergence and can collect larger rewards than the single versions.