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Volume 7 Issue 4
Jun.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Lan Jiang, Hongyun Huang and Zuohua Ding, "Path Planning for Intelligent Robots Based on Deep Q-learning With Experience Replay and Heuristic Knowledge," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1179-1189, July 2020. doi: 10.1109/JAS.2019.1911732
Citation: Lan Jiang, Hongyun Huang and Zuohua Ding, "Path Planning for Intelligent Robots Based on Deep Q-learning With Experience Replay and Heuristic Knowledge," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1179-1189, July 2020. doi: 10.1109/JAS.2019.1911732

Path Planning for Intelligent Robots Based on Deep Q-learning With Experience Replay and Heuristic Knowledge

doi: 10.1109/JAS.2019.1911732
Funds:  This work was supported by the National Natural Science Foundation of China (61751210, 61572441)
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  • Path planning and obstacle avoidance are two challenging problems in the study of intelligent robots. In this paper, we develop a new method to alleviate these problems based on deep Q-learning with experience replay and heuristic knowledge. In this method, a neural network has been used to resolve the “curse of dimensionality” issue of the Q-table in reinforcement learning. When a robot is walking in an unknown environment, it collects experience data which is used for training a neural network; such a process is called experience replay. Heuristic knowledge helps the robot avoid blind exploration and provides more effective data for training the neural network. The simulation results show that in comparison with the existing methods, our method can converge to an optimal action strategy with less time and can explore a path in an unknown environment with fewer steps and larger average reward.

     

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    Highlights

    • Fast convergence and Better strategy
      The simulation results show that in comparison with the existing methods, our method can converge to an optimal action strategy with less time and can explore a path in an unknown environment with fewer steps and larger average reward.
    • Deep Q-learning
      We use DQL to process the state information of intelligent robots, and get the cumulative reward value of the corresponding action, so as to replace Q-table in reinforcement learning and solve the “curse of dimensionality”.
    • Experience replay
      Training a neural network requires a lot of data, but when the robot explores in a unknown environment, it is impossible to prepare enough training sample sets for it in advance. We make the robot collect experience data that are generated during its moving and store them in replay memory . Then, we use the data in the replay memory to train the neural network.
    • Heuristic knowledge
      On the one hand, heuristic knowledge guides the behavior of the robot, and it makes the actions selected by the robot more purposeful; on the other hand, it also increases the effectiveness of training the neural networks. With the help of heuristic knowledge, neural networks will converge to an optimal action strategy faster.

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