EDA-RL: Estimation of distribution algorithms for reinforcement learning problems

Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009

Volumn , Issue , 2009, Pages 405-412

  Handa, Hisashi ^a  

^a OKAYAMA UNIVERSITY   (Japan)

Author keywords

Conditional random fields; Estimation of distribution algorithms; Reinforcement learning problems

Indexed keywords

CONDITIONAL PROBABILITIES; CONDITIONAL PROBABILITY DISTRIBUTIONS; CONDITIONAL RANDOM FIELD; CONVENTIONAL REINFORCEMENT LEARNING; ESTIMATION OF DISTRIBUTION ALGORITHMS; EVOLUTIONARY COMPUTATIONS; INPUT-OUTPUT DATA; MAZE PROBLEMS; PERCEPTUAL ALIASING; PROBABILISTIC DISTRIBUTION; PROBABILISTIC MODELS; TRANSITION PROBLEMS;

AUTONOMOUS AGENTS; CALCULATIONS; CONTENT BASED RETRIEVAL; EDUCATION; ESTIMATION; EVOLUTIONARY ALGORITHMS; IMAGE SEGMENTATION; PROBABILITY DISTRIBUTIONS; REINFORCEMENT; REINFORCEMENT LEARNING; SIMULATORS;

LEARNING ALGORITHMS;

EID: 72749094980     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1569901.1569958     Document Type: Conference Paper

References (12)

1. M. V. Butz and M. Pelikan. Studying XCS/BOA learning in boolean functions: structure encoding and random boolean functions. In Proc. of the 2006 Genetic and Evol. Gomput. Conf., pages 1449-1456, 2006.
2. H. Handa and M. Isozaki. Evolutionary fuzzy systems for generating better Ms.PacMan players. 2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'08), pages 2182-2185, 2008.
3. J. Lafferty. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of 18th International Conference on Machine Learning, pages 282-289. Morgan Kaufmarm, 2001.
4. P. Larrañaga and J. A. Lozano. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Norwell, MA, USA, 2001.
5. H. Mühlenbein and T. Mahnig. FDA -a scalable evolutionary algorithm for the optimization of additively decomposed functions. Evol. Comput., 7(4):353-376, 1999.
6. I. Ono, T. Nijo, and N. Ono. A genetic algorithm for automatically designing modular reinforcement learning agents. In Proc. of the Genetic and Evol. Comput. Conf., pages 203-210, 2000.
7. R. Santana. Estimation of distribution algorithms with Kikuchi approximations. Evol. Comput., 13(1):67-97, 2005.
8. S. K. Shakya, J. A. W. McCall, and D. F. Brown. Incorporating a metropolis method in a distribution estimation using markov random field algorithm. In Proc. of 2005 IEEE Congress on Evol. Comput., volume 3, pages 2576-2583, 2005.
9. S. K. Shakya, J. A. W. McCall, and D. F. Brown. Using a markov network model in a univariate EDA: An emperical cost-benefit analysis. In Proc. of 2005 Genetic and Evol. Comput. Conf., pages 727-734, 2005.
10. C. Sutton and A. Mccallum. An introduction to conditional random fields for relational learning. In L. Getoor and B. Taskar, editors, Introduction to Statistical Relational Learning, chapter 4, pages 93-128. MIT Press, Cambridge, MA, 2007.
11. R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, 1998.
12. C. Watkins and P. Dayan. Technical note: Q-learning. Machine Learning, 08:279 292(14), May 1992.