21 code implementations • 19 Feb 2015 • John Schulman, Sergey Levine, Philipp Moritz, Michael. I. Jordan, Pieter Abbeel
We describe an iterative procedure for optimizing policies, with guaranteed monotonic improvement.
17 code implementations • 8 Jun 2015 • John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, Pieter Abbeel
Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear function approximators such as neural networks.
1 code implementation • 9 Aug 2015 • Philipp Moritz, Robert Nishihara, Michael. I. Jordan
We propose a new stochastic L-BFGS algorithm and prove a linear convergence rate for strongly convex and smooth functions.
1 code implementation • 19 Nov 2015 • Philipp Moritz, Robert Nishihara, Ion Stoica, Michael. I. Jordan
We introduce SparkNet, a framework for training deep networks in Spark.
2 code implementations • 11 Mar 2017 • Robert Nishihara, Philipp Moritz, Stephanie Wang, Alexey Tumanov, William Paul, Johann Schleier-Smith, Richard Liaw, Mehrdad Niknami, Michael. I. Jordan, Ion Stoica
Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making.
4 code implementations • 16 Dec 2017 • Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael. I. Jordan, Ion Stoica
To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system's control state.
3 code implementations • ICML 2018 • Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael. I. Jordan, Ion Stoica
Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation.
4 code implementations • 13 Jul 2018 • Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E. Gonzalez, Ion Stoica
We show that this interface meets the requirements for a broad range of hyperparameter search algorithms, allows straightforward scaling of search to large clusters, and simplifies algorithm implementation.
no code implementations • 7 Apr 2019 • Thomas Anthony, Robert Nishihara, Philipp Moritz, Tim Salimans, John Schulman
Monte Carlo Tree Search (MCTS) algorithms perform simulation-based search to improve policies online.
1 code implementation • 13 Feb 2020 • Siyuan Zhuang, Zhuohan Li, Danyang Zhuo, Stephanie Wang, Eric Liang, Robert Nishihara, Philipp Moritz, Ion Stoica
Task-based distributed frameworks (e. g., Ray, Dask, Hydro) have become increasingly popular for distributed applications that contain asynchronous and dynamic workloads, including asynchronous gradient descent, reinforcement learning, and model serving.