Study on Decentralized Learning Algorithms in Markovian Environments

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The results of this study are summarized as follows :(1) We proposed a new decentralized learning algorithm for Markov chains with unknown dynamics. A detailed simulation study revealed the feasibility of our algorithm and its superiority to the Q-learning scheme.(2) We proposed an object-oriented design support system for developing autonomous mobile robots. The usefulness of our support system was examined through some implementations of simulated and real robots. By using this support system, we also developed a robot which can acquire a proper setting of gain factors in an obstacle avoidance algorithm, called the VFH,by learning.(3) We applied the decentralized learning algorithm to the problem of adaptive action selection in an intelligent mobile robot. We employed a robot which has two photosensors to measure the light intensity in right or left direction. The robot's task is to learn an action selection policy for moving toward and getting to a light placed in any location of a room. The decentralized learning approach was successfully tested by running simulated robots, which were implemented by the object-oriented design support system.(4) We proposed a class of hierarchical systems called holon networks as general models for identification of nonlinear dynamical systems. Holon networks are able to evolve by self-organizing their structure and learn nonlinear systems without assuming much knowledge of them.