Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Proceedings of the European Conference on Genetic Programming
PADO: Learning Tree Structured Algorithms for Orchestration into an Object Recognition System
PADO: Learning Tree Structured Algorithms for Orchestration into an Object Recognition System
Linkage Problem, Distribution Estimation, and Bayesian Networks
Evolutionary Computation
Introducing assignment functions to Bayesian optimization algorithms
Information Sciences: an International Journal
Probabilistic incremental program evolution
Evolutionary Computation
EDA-RL: estimation of distribution algorithms for reinforcement learning problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A novel EDAs based method for HP model protein folding
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Influence of selection on structure learning in markov network EDAs: an empirical study
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Extended rule-based genetic network programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Classical EDAs generally use truncation selection to estimate the distribution of the feasible (good) individuals while ignoring the infeasible (bad) ones. However, various research in EAs reported that the infeasible individuals may affect and help the problem solving. This paper proposed a new method to use the infeasible individuals by studying the sub-structures rather than the entire individual structures to solve Reinforcement Learning (RL) problems, which generally factorize their entire solutions to the sequences of state-action pairs. This work was studied in a recent graph-based EDA named Probabilistic Model Building Genetic Network Programming (PMBGNP) which can solve RL problems successfully. The effectiveness of this work is verified in a RL problem, i.e., robot control, comparing with some other related work.