NIPS Proceedingsβ

Bruno Scherrer

5 Papers

  • Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning (2018)
  • Approximate Dynamic Programming Finally Performs Well in the Game of Tetris (2013)
  • Improved and Generalized Upper Bounds on the Complexity of Policy Iteration (2013)
  • On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes (2012)
  • Biasing Approximate Dynamic Programming with a Lower Discount Factor (2008)