Matrix computations (3rd ed.)
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Least-squares policy iteration
The Journal of Machine Learning Research
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Fast gradient-descent methods for temporal-difference learning with linear function approximation
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Reinforcement learning of local shape in the game of go
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
PEGASUS: A Peta-Scale Graph Mining System Implementation and Observations
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Hadoop: The Definitive Guide
Distributed nonnegative matrix factorization for web-scale dyadic data analysis on mapreduce
Proceedings of the 19th international conference on World wide web
Twister: a runtime for iterative MapReduce
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Data-Intensive Text Processing with MapReduce
Data-Intensive Text Processing with MapReduce
Algorithms for Reinforcement Learning
Algorithms for Reinforcement Learning
HaLoop: efficient iterative data processing on large clusters
Proceedings of the VLDB Endowment
Regression methods for pricing complex American-style options
IEEE Transactions on Neural Networks
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We investigate the parallelization of reinforcement learning algorithms using MapReduce, a popular parallel computing framework. We present parallel versions of several dynamic programming algorithms, including policy evaluation, policy iteration, and off-policy updates. Furthermore, we design parallel reinforcement learning algorithms to deal with large scale problems using linear function approximation, including model-based projection, least squares policy iteration, temporal difference learning and recent gradient temporal difference learning algorithms. We give time and space complexity analysis of the proposed algorithms. This study demonstrates how parallelization opens new avenues for solving large scale reinforcement learning problems.