Search Results for author: Jingda Wu

Found 5 papers, 2 papers with code

Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning

no code implementations1 Jul 2022 Jingda Wu, Wenhui Huang, Niels de Boer, Yanghui Mo, Xiangkun He, Chen Lv

Decisions made by human subjects in a driving simulator are treated as safe demonstrations, which are stored into the replay buffer and then utilized to enhance the training process of RL.

Autonomous Driving Decision Making +1

Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration

no code implementations20 Jun 2022 Wenhui Huang, Cong Zhang, Jingda Wu, Xiangkun He, Jie Zhang, Chen Lv

We theoretically prove that the policy improvement theorem holds for the preference-guided $\epsilon$-greedy policy and experimentally show that the inferred action preference distribution aligns with the landscape of corresponding Q-values.

Q-Learning reinforcement-learning +1

Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving

1 code implementation26 Sep 2021 Jingda Wu, Zhiyu Huang, Wenhui Huang, Chen Lv

A novel prioritized experience replay mechanism that adapts to human guidance in the reinforcement learning process is proposed to boost the efficiency and performance of the reinforcement learning algorithm.

Autonomous Driving reinforcement-learning +1

Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Driving

no code implementations23 Jun 2021 Jingda Wu, Zhiyu Huang, Chen Lv

Then, a novel uncertainty-aware model-based RL framework is developed based on the adaptive truncation approach, providing virtual interactions between the agent and environment model, and improving RL's training efficiency and performance.

Autonomous Driving Model-based Reinforcement Learning +2

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