An N-step Look Ahead Algorithm Using Mixed (On and Off) Policy Reinforcement Learning

Kuchibhotla, Vivek; P, Harshitha; Goyal, Shobhit

doi:10.1109/iciss49785.2020.9315959

Cited by 1 publication

(1 citation statement)

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“…In deep reinforcement learning, the single-step average reward value of each episode is an important indicator to measure the training effect [ 30 , 31 , 32 , 33 ]. This paper counts the average single-step rewards of [ 22 ] and DCPER-DDPG algorithm in 6000 episodes.…”

Section: Results Analysismentioning

confidence: 99%

Lane Following Method Based on Improved DDPG Algorithm

Zhang

et al. 2021

Sensors

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In an autonomous vehicle, the lane following algorithm is an important component, which is a basic function of autonomous driving. However, the existing lane following system has a few shortcomings: first, the control method it adopts requires an accurate system model, and different vehicles have different parameters, which needs a lot of parameter calibration work. The second is that it may fail on road sections where the lateral acceleration requirements of vehicles are large, such as large curves. Third, its decision-making system is defined based on rules, which has disadvantages: it is difficult to formulate; human subjective factors cannot guarantee objectivity; coverage is difficult to guarantee. In recent years, the deep deterministic policy gradient (DDPG) algorithm has been widely used in the field of autonomous driving due to its strong nonlinear fitting ability and generalization performance. However, the DDPG algorithm has overestimated state action values and large cumulative errors, low training efficiency and other issues. Therefore, this paper improves the DDPG algorithm based on the double critic networks and priority experience replay mechanism. Then this paper proposes a lane following method based on this algorithm. Experiment shows that the algorithm can achieve excellent following results under various road conditions.

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Section: Results Analysismentioning

confidence: 99%

Lane Following Method Based on Improved DDPG Algorithm

Zhang

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

An N-step Look Ahead Algorithm Using Mixed (On and Off) Policy Reinforcement Learning

Cited by 1 publication

References 3 publications

Lane Following Method Based on Improved DDPG Algorithm

Lane Following Method Based on Improved DDPG Algorithm

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