<--- Back to Details
First PageDocument Content
Dynamic programming / Stochastic control / Markov models / Statistical mechanics / Partially observable Markov decision process / Monte Carlo method / Reinforcement learning / Markov decision process / Automated planning and scheduling / Statistics / Probability and statistics / Markov processes
Date: 2010-12-04 14:20:24
Dynamic programming
Stochastic control
Markov models
Statistical mechanics
Partially observable Markov decision process
Monte Carlo method
Reinforcement learning
Markov decision process
Automated planning and scheduling
Statistics
Probability and statistics
Markov processes

Add to Reading List

Source URL: books.nips.cc

Download Document from Source Website

File Size: 438,50 KB

Share Document on Facebook

Similar Documents

Concurrent computing / Parallel computing / Computing / IT infrastructure / Cloud infrastructure / Job scheduling / Apache Hadoop / Apache Software Foundation / Data-intensive computing / Workflow / Algorithmic skeleton / Programming paradigm

Towards a high level programming paradigm to deploy e-science applications with dynamic workflows on large scale distributed systems Mohamed Ben Belgacem Nabil Abdennadher

DocID: 1xTOs - View Document

Minimax Differential Dynamic Programming: An Application to Robust Biped Walking Jun Morimoto Human Information Science Labs, Department 3, ATR International

DocID: 1vqMk - View Document

Empirical Dynamic Programming William B. Haskell ISE Department, National University of Singapore Rahul Jain*

DocID: 1vouJ - View Document

MarchRevised MayReport LIDS-P-3506 Stable Optimal Control and Semicontractive Dynamic Programming

DocID: 1vhRF - View Document

EE365: Deterministic Finite State Control Deterministic optimal control Shortest path problem Dynamic programming Examples

DocID: 1vg0M - View Document