Incremental Pruning: A simple, Fast, Exact Method for Partially Observable Markov Decision Processes Anthony Cassandra Computer Science Dept. Brown University.
R-Max: A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
Basic Concepts
Maximum Expected Utility
Reinforcement Learning (RL) Consider an “agent” embedded in an environmentConsider an “agent” embedded in an environment Task of the agentTask of the agent.
Copyright (c) 2000 by Harcourt, Inc. All rights reserved. Basic Concepts Any situation in which individuals must make strategic choices and in which the.
AIMA 25.1-25.4 Introduction to Robotics Presented by Derek Colla [additions by Simon Levy]
Reinforcement Learning (RL)
Pre-Bayesian Games Moshe Tennenholtz Technion—Israel Institute of Technology.
V Michael Benedikt Juliana Freire Patrice Godefroid Presented by Krist Wongsuphasawat eriWeb Automatically Testing Dynamic Web Sites.
Chapter 12 Strategy and Game Theory © 2004 Thomson Learning/South-Western.
Classical Planning Chapter 10 Mausam / Andrey Kolobov (Based on slides of Dan Weld, Marie desJardins)
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