Research on Path Planning of Mobile Robots Based on Deep Reinforcement Learning
Given the problem of slow convergence speed when using Deep Reinforcement Learning algorithms for mobile robot path planning,an improved algorithm is proposed.It designs the learning potential score of samples in the experience replay mechanism,prioritizes the samples based on the learning potential score,and samples them according to the score.It applies improved algorithms to robot path planning tasks and designs reward functions,obstacle avoidance parameters,and path planning experimental environments.Through experimental comparison with comparative algorithms,the convergence speed of the improved algorithm and its effectiveness in path planning tasks are verified.
Deep Reinforcement Learningpath planningmobile robot
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