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Deep Q Network (DQN) has several limitations when applied in planning a pathin environment with a number of dilemmas according to our experiment. Thereward function may be hard to model, and successful experience transitions aredifficult to find in experience replay. In this context, this paper proposes animproved Double DQN (DDQN) to solve the problem by reference to A* andRapidly-Exploring Random Tree (RRT). In order to achieve the rich experimentsin experience replay, the initialization of robot in each training round isredefined based on RRT strategy. In addition, reward for the free positions isspecially designed to accelerate the learning process according to thedefinition of position cost in A*. The simulation experimental results validatethe efficiency of the improved DDQN, and robot could successfully learn theability of obstacle avoidance and optimal path planning in which DQN or DDQNhas no effect.

An Improved Algorithm of Robot Path Planning in Complex Environment Based on Double DQN