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When apply Q-learning to complex real-world problems, the learning process is long enough to make this method unpractical. The major cause is Q-learning requires the agent to visit every state-action transition infinitely often for making Q value convergent. We propose a state-cluster based Q-learning method to accelerate convergence and shorten learning process. This method creates the state-cluster...
To hire multiple agents cooperating to solve realworld problem with large state space, a precondition is to provide an interaction medium for knowledge exchange and share among agents. We propose an interaction medium: State-Clusters, computed from the state trajectory that the agent wandered in state space. The State-Clusters of a state includes acyclic state paths from other states to this state,...
This investigation was performed to see the regularity of parameters in the modified mixture of experts (MoE) to direct parameter selection in real applications. The experiment was performed on the mean of monthly precipitation in May from 1956a through 2004a in a Chinese region. We got stable parameter combinations through testing on various combinations of parameters in various value ranges, and...
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