1 code implementation • IEEE Transactions on Neural Networks and Learning Systems 2023 • Wenhui Huang, Cong Zhang, Jingda Wu, Xiangkun He, Jie Zhang, Chen Lv.
Stochastic exploration is the key to the success of the Deep Q-network (DQN) algorithm.
no code implementations • 1 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.
1 code implementation • 20 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.
1 code implementation • 30 Jan 2022 • Qi Liu, Zirui Li, Xueyuan Li, Jingda Wu, Shihua Yuan
Several GRL approaches are summarized and implemented in the proposed framework.
1 code implementation • 26 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.
no code implementations • 23 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.