Modeling and Simulation of UAV Autonomous Obstacle Avoidance Based on DQN

Fang, Jiayong; Zhou, Zucheng; Wang, Huanbin; Sheng, Sheng; Chen, Shitao

doi:10.1145/3508546.3508620

Cited by 1 publication

(1 citation statement)

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“…Another is that each experience can be learned repeatedly which improves the utilization rate of experience. DQN uses two networks with the same structure but different parameters [10]. One is used to predict Q estimation.…”

Section: Iccee-2022mentioning

confidence: 99%

The Determination of Reward Function in AGV Motion Control Based on DQN

Chen

Zhong³

et al. 2022

J. Phys.: Conf. Ser.

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Motion control is a very important part in the field of AGV(Automated Guided Vehicle). A good motion control method can make the movement of AGV more stable. Network models of reinforcement learning is one of the methods to solve the problem of AGV in motion control. This paper introduces the Markov Decision Process and the role of reward function. Besides, it studies and analyzes several classic reinforcement learning cases. DQN(Deep Q-Learning Network) which belongs to deep reinforcement learning network model is adopted. We set up several sets of comparative experiments with different reward functions by using sparse reward setting method, formalized reward setting method and reward coefficient variation reward setting method. Also we adjust the time of training. Through comparison, a reward function suitable for solving the problem of AGV motion control is obtained. In the field of AGV motion control, reinforcement learning model can converge faster and make more correct decisions. The reward function is verified in the simulation environment built by ROS(Robot Operating System).

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Section: Iccee-2022mentioning

confidence: 99%