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In this paper we propose Q-learning with continuous action space and extend this algorithm to a multi-agent system. Conventional Q-learning needs a pre-defined and discrete state space. But it is not practical because the states of the environment in the real world and actions are both continuous. The algorithm will use a concept that is similar to the SRV (Stochastic Real-Valued Unit) to train the...
Q-learning, a most widely used reinforcement learning method, normally needs well-defined quantized state and action spaces to obtain an optimal policy for accomplishing a given task. This means it difficult to be applied to real robot tasks because of poor performance of learned behavior due to the failure of quantization of continuous state and action spaces. In this paper, we proposed a fuzzy-based...
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