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.
|ホスト出版物のタイトル||Genetic and Evolutionary Computation Conference, GECCO'11|
|出版ステータス||Published - 2011|
|イベント||13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 - Dublin|
継続期間: 2011 7月 12 → 2011 7月 16
|Other||13th Annual Genetic and Evolutionary Computation Conference, GECCO'11|
|Period||11/7/12 → 11/7/16|
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