Abstract
A algorithm of dynamic multi-step reinforcement learning based on virtual potential field path planning is proposed in this paper. Firstly, it is constructed the virtual potential field according to the known information. And then in view of \( Q \) learning algorithm of the \( Q\left( \lambda \right) \) algorithm, a multi-step reinforcement learning algorithm is proposed in this paper. It can update current \( Q \) value used of future dynamic \( k \) steps according to the current environment status. At the same time, the convergence is analyzed. Finally the simulation experiments are done. It shows that the proposed algorithm and convergence and so on are more efficiency than similar algorithms.
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