3k citations · 6.2k across the 11 of their papers we have counts for
4 papers · 1 filter
Policy Gradient Search: Online Planning and Expert Iteration without Search Trees
Thomas Anthony, Robert Nishihara, Philipp Moritz +2
Monte Carlo Tree Search (MCTS) algorithms perform simulation-based search to improve policies online. During search, the simulation policy is adapted to explore the most promising…
Tune: A Research Platform for Distributed Model Selection and Training
Richard Liaw, Eric Liang, Robert Nishihara +3
Modern machine learning algorithms are increasingly computationally demanding, requiring specialized hardware and distributed computation to achieve high performance in a reasonabl…
High-Dimensional Continuous Control Using Generalized Advantage Estimation
John Schulman, Philipp Moritz, Sergey Levine +2
Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear fu…
Trust Region Policy Optimization
John Schulman, Sergey Levine, Philipp Moritz +2
We describe an iterative procedure for optimizing policies, with guaranteed monotonic improvement. By making several approximations to the theoretically-justified procedure, we dev…