Unmanned driving path planning based on deep reinforcement learning
Aiming at solving the problems of unstable convergence and low learning efficiency of the Deep Determinis-tic Policy Gradient(DDPG)algorithm when training neural networks,a Reward Guidance DDPG(RG_DDPG)algorithm was proposed.The algorithm creates a set of excellent experience in the round,which is convenient to guide the intelli-gent car to make full use of the past effective information and obtain a stable control strategy.The reward-based priority experience playback mechanism is adopted to break the correlation between data,improve the utilization rate of data,re-duce the blindness of the search process,and improve the convergence stability of the algorithm.The algorithm is veri-fied based on Robot Operating System(ROS)operating system.In the Gazebo modeling software,the intelligent car mod-el and the obstacle environment are designed.Use decision-making algorithms to plan safe driving paths for intelligent cars.The data results verify the effectiveness of the RG_DDPG algorithm in handling path planning tasks.Compared with the DDPG algorithm,the speed of the improved intelligent car can be increased by 60.5%,the reward obtained is more than doubled,and the convergence stability of the algorithm is better.Finally,the feasibility of the algorithm is veri-fied by real vehicle experiments.
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