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- W4285324524 abstract "Deep reinforcement learning is a popular and effective approach for autonomous driving to achieve safe and complex self-driving with little or no human input. However, in the random and intricate driving scenarios with surrounding traffic, conventional RL-algorithms based on greedy policy might be unstable and inefficient in training because of their lack in risk sensitivity. This paper proposes an RL-learning approach for autonomous driving which includes the estimation of expected cumulative future reward and its standard deviation. We utilize the difference between expectation and standard deviation as the decision foundation to improve the risk sensitivity of policy and training performance. The proposed algorithm is implemented in the CARLA simulation environment and the results demonstrate that vehicle agent based on our learning algorithm is able to learn more efficiently and drive more safely." @default.
- W4285324524 created "2022-07-14" @default.
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- W4285324524 date "2021-12-03" @default.
- W4285324524 modified "2023-10-18" @default.
- W4285324524 title "A Mean-variance Optimization DQN in Autonomous Driving" @default.
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- W4285324524 doi "https://doi.org/10.1145/3518781.3519208" @default.
- W4285324524 hasPublicationYear "2021" @default.
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