An efficient reinforcement learning algorithm for continuous actions

In this paper a fast and effective reinforcement learning algorism named Dyna-CA in which the learning agent or agents can get the continuous action has been proposed to get the generalization of reinforcement learning methods to large-scale or continuous space. Firstly, the set of k states around the current state will be observed and the probability distribution of k states on condition current state can be calculated by functional mapping. Secondly the selection action-making in the current state for agent is recommended the weighted sum of best actions taken in the neighbor states to guarantee the learning with continuous actions. Then the Q value will be updated by the rules of Dyna algorism, which are not only based on the current neighbor states´ practical knowledge but also the priori neighbor states´ experience. Computer simulations involving the Maze and Acrobat problems illustrate the validity of the proposed reinforcement learning method and fast convergence in learning an optimal policy.