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IJCAI
2007
13 years 9 months ago
Using Linear Programming for Bayesian Exploration in Markov Decision Processes
A key problem in reinforcement learning is finding a good balance between the need to explore the environment and the need to gain rewards by exploiting existing knowledge. Much ...
Pablo Samuel Castro, Doina Precup
GECCO
2003
Springer
107views Optimization» more  GECCO 2003»
14 years 23 days ago
Exploration of a Two Sided Rendezvous Search Problem Using Genetic Algorithms
The problem of searching for a walker that wants to be found, when the walker moves toward the helicopter when it can hear it, is an example of a two sided search problem which is ...
T. Q. S. Truong, A. Stacey
WSC
2004
13 years 9 months ago
Stochastic Approximation with Simulated Annealing as an Approach to Global Discrete-Event Simulation Optimization
This paper explores an approach to global, stochastic, simulation optimization which combines stochastic approximation (SA) with simulated annealing (SAN). SA directs a search of ...
Matthew H. Jones, K. Preston White
AAIM
2005
Springer
110views Algorithms» more  AAIM 2005»
14 years 1 months ago
Dynamically Updating the Exploiting Parameter in Improving Performance of Ant-Based Algorithms
Abstract. The utilization of pseudo-random proportional rule to balance between the exploitation and exploration of the search process was shown in Ant Colony System (ACS) algorith...
Hoang Trung Dinh, Abdullah Al Mamun, Hieu T. Dinh
COGSR
2011
71views more  COGSR 2011»
13 years 2 months ago
Psychological models of human and optimal performance in bandit problems
In bandit problems, a decision-maker must choose between a set of alternatives, each of which has a fixed but unknown rate of reward, to maximize their total number of rewards ov...
Michael D. Lee, Shunan Zhang, Miles Munro, Mark St...