Brain-computer interfaces(BCI)use neural activity as a control signal to enable direct communication between the human brain and external devices.The electrical signals generated by the brain are captured through elec...Brain-computer interfaces(BCI)use neural activity as a control signal to enable direct communication between the human brain and external devices.The electrical signals generated by the brain are captured through electroencephalogram(EEG)and translated into neural intentions reflecting the user’s behavior.Correct decoding of the neural intentions then facilitates the control of external devices.Reinforcement learning-based BCIs enhance decoders to complete tasks based only on feedback signals(rewards)from the environment,building a general framework for dynamic mapping from neural intentions to actions that adapt to changing environments.However,using traditional reinforcement learning methods can have challenges such as the curse of dimensionality and poor generalization.Therefore,in this paper,we use deep reinforcement learning to construct decoders for the correct decoding of EEG signals,demonstrate its feasibility through experiments,and demonstrate its stronger generalization on motion imaging(MI)EEG data signals with high dynamic characteristics.展开更多
Reinforcement learning(RL)can free automated vehicles(AVs)from the car-following constraints and provide more possible explorations for mixed behavior.This study uses deep RL as AVs’longitudinal control and designs a...Reinforcement learning(RL)can free automated vehicles(AVs)from the car-following constraints and provide more possible explorations for mixed behavior.This study uses deep RL as AVs’longitudinal control and designs a multi-level objectives framework for AVs’trajectory decision-making based on multi-agent DRL.The saturated signalized intersection is taken as the research object to seek the upper limit of traffic efficiency and realize the specific target control.The simulation results demonstrate the convergence of the proposed framework in complex scenarios.When prioritizing throughputs as the primary objective and emissions as the secondary objective,both indicators exhibit a linear growth pattern with increasing market penetration rate(MPR).Compared with MPR is 0%,the throughputs can be increased by 69.2%when MPR is 100%.Compared with linear adaptive cruise control(LACC)under the same MPR,the emissions can also be reduced by up to 78.8%.Under the control of the fixed throughputs,compared with LACC,the emission benefits grow nearly linearly as MPR increases,it can reach 79.4%at 80%MPR.This study employs experimental results to analyze the behavioral changes of mixed flow and the mechanism of mixed autonomy to improve traffic efficiency.The proposed method is flexible and serves as a valuable tool for exploring and studying the behavior of mixed flow behavior and the patterns of mixed autonomy.展开更多
Anthropomorphic hand manipulation is a quintessential example of embodied intelligence in robotics,presenting a notable challenge due to its high degrees of freedom and complex inter-joint coupling.Though recent advan...Anthropomorphic hand manipulation is a quintessential example of embodied intelligence in robotics,presenting a notable challenge due to its high degrees of freedom and complex inter-joint coupling.Though recent advancements in reinforcement learning(RL)have led to substantial progress in this field,existing methods often overlook the detailed structural properties of anthropomorphic hands.To address this,we propose a novel deep RL approach,Bionic-Constrained Diffusion Policy(Bio-CDP),which integrates knowledge of human hand control with a powerful diffusion policy representation.Our bionic constraint modifies the action space of anthropomorphic hand control,while the diffusion policy enhances the expressibility of the policy in high-dimensional continuous control tasks.Bio-CDP has been evaluated in the simulation environment,where it has shown superior performance and data efficiency compared to state-of-the-art RL approaches.Furthermore,our method is resilient to task complexity and robust in performance,making it a promising tool for advanced control in robotics.展开更多
文摘Brain-computer interfaces(BCI)use neural activity as a control signal to enable direct communication between the human brain and external devices.The electrical signals generated by the brain are captured through electroencephalogram(EEG)and translated into neural intentions reflecting the user’s behavior.Correct decoding of the neural intentions then facilitates the control of external devices.Reinforcement learning-based BCIs enhance decoders to complete tasks based only on feedback signals(rewards)from the environment,building a general framework for dynamic mapping from neural intentions to actions that adapt to changing environments.However,using traditional reinforcement learning methods can have challenges such as the curse of dimensionality and poor generalization.Therefore,in this paper,we use deep reinforcement learning to construct decoders for the correct decoding of EEG signals,demonstrate its feasibility through experiments,and demonstrate its stronger generalization on motion imaging(MI)EEG data signals with high dynamic characteristics.
基金supported by the National Natural Science Foundation of China(Grant Nos.52272332 and 51578199)Heilongjiang Provincial Natural Science Foundation(Grant No.YQ2021E031)the Fundamental Research Funds for the Central Universities(Grant No.HIT.OCEF.2022026).
文摘Reinforcement learning(RL)can free automated vehicles(AVs)from the car-following constraints and provide more possible explorations for mixed behavior.This study uses deep RL as AVs’longitudinal control and designs a multi-level objectives framework for AVs’trajectory decision-making based on multi-agent DRL.The saturated signalized intersection is taken as the research object to seek the upper limit of traffic efficiency and realize the specific target control.The simulation results demonstrate the convergence of the proposed framework in complex scenarios.When prioritizing throughputs as the primary objective and emissions as the secondary objective,both indicators exhibit a linear growth pattern with increasing market penetration rate(MPR).Compared with MPR is 0%,the throughputs can be increased by 69.2%when MPR is 100%.Compared with linear adaptive cruise control(LACC)under the same MPR,the emissions can also be reduced by up to 78.8%.Under the control of the fixed throughputs,compared with LACC,the emission benefits grow nearly linearly as MPR increases,it can reach 79.4%at 80%MPR.This study employs experimental results to analyze the behavioral changes of mixed flow and the mechanism of mixed autonomy to improve traffic efficiency.The proposed method is flexible and serves as a valuable tool for exploring and studying the behavior of mixed flow behavior and the patterns of mixed autonomy.
基金supported by the National Nature Science Foundation of China under grants 91748131,6200629,61771471,and 62073324in part by the InnoHK Project.
文摘Anthropomorphic hand manipulation is a quintessential example of embodied intelligence in robotics,presenting a notable challenge due to its high degrees of freedom and complex inter-joint coupling.Though recent advancements in reinforcement learning(RL)have led to substantial progress in this field,existing methods often overlook the detailed structural properties of anthropomorphic hands.To address this,we propose a novel deep RL approach,Bionic-Constrained Diffusion Policy(Bio-CDP),which integrates knowledge of human hand control with a powerful diffusion policy representation.Our bionic constraint modifies the action space of anthropomorphic hand control,while the diffusion policy enhances the expressibility of the policy in high-dimensional continuous control tasks.Bio-CDP has been evaluated in the simulation environment,where it has shown superior performance and data efficiency compared to state-of-the-art RL approaches.Furthermore,our method is resilient to task complexity and robust in performance,making it a promising tool for advanced control in robotics.
