Abstract:

In view of the disadvantages of traditional reinforcement learning methods in motion planning, especially the problem of robot obstacle avoidance, it is easy to have overestimation and difficult to adapt to complex environment. A new model based on deep reinforcement learning is proposed to improve the obstacle avoidance performance of robots. The model combines dueling networks with Q-learning which is the traditional reinforcement learning method, and using two independent trained dueling networks to deal with environmental data and predict the action value. In the output layer, the state value and the action advantage are output respectively, with both values combined as the final action value. The model can process high dimension data to adapt to complex and changeable environment, and output advantageous actions for robot selection to get a higher accumulative reward. It can effectively improve the obstacle avoidance performance of a robot.

Key words: robot obstacle avoidance, deep reinforcement learning, dueling networks, independent trained