DetH*: Approximate Hierarchical Solution of Large Markov Decision Processes∗ Jennifer L. Barry, Leslie Pack Kaelbling, Tom´as Lozano-P´erez MIT Computer Science and Artificial Intelligence Laboratory Cambridge, MA 02

First Page		Document Content
Date: 2011-09-29 15:55:17 Dynamic programming Markov processes Stochastic control Optimal control Markov decision process Reinforcement learning Function Macro Automated planning and scheduling Statistics Control theory Mathematics		DetH*: Approximate Hierarchical Solution of Large Markov Decision Processes∗ Jennifer L. Barry, Leslie Pack Kaelbling, Tom´as Lozano-P´erez MIT Computer Science and Artificial Intelligence Laboratory Cambridge, MA 02 Add to Reading List Source URL: people.csail.mit.edu Download Document from Source Website File Size: 309,32 KB Share Document on Facebook

	Cooperative Multi-Agent Control Using Deep Reinforcement Learning Jayesh K. Gupta Maxim Egorov DocID: 1xVVh - View Document
	Distributed Computing Prof. R. Wattenhofer SA/MA: Byzantine Reinforcement Learning DocID: 1xVKs - View Document
	Distributed Computing Prof. R. Wattenhofer Generating CAPTCHAs with Deep (Reinforcement) Learning DocID: 1xV3l - View Document
	Multi-step Bootstrapping Jennifer She Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto February 7, 2017 DocID: 1xUBi - View Document
	Cellular Network Traffic Scheduling using Deep Reinforcement Learning Sandeep Chinchali, et. al. Marco Pavone, Sachin Katti Stanford University AAAI 2018 DocID: 1xUAT - View Document