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Keywords = heuristic knowledge (HK)

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17 pages, 5864 KiB  
Article
Deep Reinforcement Learning for Autonomous Driving with an Auxiliary Actor Discriminator
by Qiming Gao, Fangle Chang, Jiahong Yang, Yu Tao, Longhua Ma and Hongye Su
Sensors 2024, 24(2), 700; https://doi.org/10.3390/s24020700 - 22 Jan 2024
Cited by 3 | Viewed by 3258
Abstract
In the research of robot systems, path planning and obstacle avoidance are important research directions, especially in unknown dynamic environments where flexibility and rapid decision makings are required. In this paper, a state attention network (SAN) was developed to extract features to represent [...] Read more.
In the research of robot systems, path planning and obstacle avoidance are important research directions, especially in unknown dynamic environments where flexibility and rapid decision makings are required. In this paper, a state attention network (SAN) was developed to extract features to represent the interaction between an intelligent robot and its obstacles. An auxiliary actor discriminator (AAD) was developed to calculate the probability of a collision. Goal-directed and gap-based navigation strategies were proposed to guide robotic exploration. The proposed policy was trained through simulated scenarios and updated by the Soft Actor-Critic (SAC) algorithm. The robot executed the action depending on the AAD output. Heuristic knowledge (HK) was developed to prevent blind exploration of the robot. Compared to other methods, adopting our approach in robot systems can help robots converge towards an optimal action strategy. Furthermore, it enables them to explore paths in unknown environments with fewer moving steps (showing a decrease of 33.9%) and achieve higher average rewards (showning an increase of 29.15%). Full article
(This article belongs to the Special Issue Advances in Sensing, Imaging and Computing for Autonomous Driving)
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