Search Results for author: Xiangkun He

Found 6 papers, 3 papers with code

Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation

1 code implementation1 Jan 2023 Wenhui Huang, Yanxin Zhou, Xiangkun He, Chen Lv

Despite some successful applications of goal-driven navigation, existing deep reinforcement learning (DRL)-based approaches notoriously suffers from poor data efficiency issue.

Autonomous Navigation Decision Making +2

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

1 code implementation20 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.

Atari Games Q-Learning +2

Approximating Pareto Frontier through Bayesian-optimization-directed Robust Multi-objective Reinforcement Learning

no code implementations1 Jan 2021 Xiangkun He, Jianye Hao, Dong Li, Bin Wang, Wulong Liu

Thirdly, the agent’s learning process is regarded as a black-box, and the comprehensive metric we proposed is computed after each episode of training, then a Bayesian optimization (BO) algorithm is adopted to guide the agent to evolve towards improving the quality of the approximated Pareto frontier.

Bayesian Optimization Diversity +2

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