Hybrid of Reinforcement and Imitation Learning for Human-Like Agents

Dossa, Rousslan Fernand Julien; Lian, Xinyu; Nomoto, Hirokazu; Matsubara, Takashi; Uehara, Kuniaki

doi:10.1587/transinf.2019edp7298

Cited by 5 publications

(1 citation statement)

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“…For automated driving research, it can make the controlled vehicle learn basic driving rules based on the driver's experience. Dossa et al (2020) proposed a hybrid automated driving model based on the reinforcement learning method with an imitation learning strategy introduced. Test results show that the hybrid model can be trained efficiently based on the driver's experience imitation.…”

Section: Introductionmentioning

confidence: 99%

An automated driving strategy generating method based on WGAIL–DDPG

Zhang¹,

Wan²,

Gang³

et al. 2021

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Reliability, efficiency and generalization are basic evaluation criteria for a vehicle automated driving system. This paper proposes an automated driving decision-making method based on the Wasserstein generative adversarial imitation learning-deep deterministic policy gradient (WGAIL-DDPG(λ)). Here the exact reward function is designed based on the requirements of a vehicle's driving performance, i.e., safety, dynamic and ride comfort performance. The model's training efficiency is improved through the proposed imitation learning strategy, and a gain regulator is designed to smooth the transition from imitation to reinforcement phases. Test results show that the proposed decision-making model can generate actions quickly and accurately according to the surrounding environment. Meanwhile, the imitation learning strategy based on expert experience and the gain regulator can effectively improve the training efficiency for the reinforcement learning model. Additionally, an extended test also proves its good adaptability for different driving conditions.

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Section: Introductionmentioning

confidence: 99%