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This paper studies the adaptability of Two-Stage Reinforcement Learning based on Genetic Network Programming for a mobile robot to cope with sudden changes in the environments, i.e., sensors break suddenly in the implementation. Two-Stage Reinforcement Learning (TSRL) uses two kinds of learning, that is, (1) sub node selection proposed in the conventional Genetic Network Programming with Reinforcement...
This paper proposes Fuzzy Genetic Network Programming with Reinforcement Learning (Fuzzy GNP-RL). This method integrates fuzzy logic to the conventional GNP-RL. The new part of the proposed method is fuzzy judgment nodes. Fuzzy GNP-RL provides flexibility to determine the appropriate next node by the probabilistic transition instead of that by the threshold values on GNP-RL. The simulation of the...
Genetic Network Programming (GNP) has been proposed as one of the evolutionary algorithms and extended with reinforcement learning (GNP-RL). The combination of evolution and learning can efficiently evolve programs and the fitness improvement has been confirmed in the simulations of tileworld problems, elevator group supervisory control systems, stock trading models and wall following behavior of...
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