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Tianle Zhang,Zhen Liu,Zhiqiang Pu,Jianqiang Yi,Yanyan Liang,Du Zhang 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.7
Although deep reinforcement learning has recently achieved some successes in robot navigation, there are still unsolved problems. Particularly, a robot guided by a distant ultimate goal is easy to get stuck in danger or encounter collisions in dynamic crowded environments due to the lack of long-term perspectives. In this paper, a novel subgoal-guided approach based on two-level hierarchical deep reinforcement learning with spatial-temporal graph attention networks (ST-GANets), called SG-HDRL, is proposed for a robot navigating in a dynamic crowded environment with autonomous obstacles, e.g., crowd. Specifically, the high-level policy, that models the spatialtemporal relation between the robot and the obstacles using the obstacles’ trajectories by the designed high-level ST-GANet, generates intermediate subgoals from a longer-term perspective over higher temporal scales. The subgoals give a favorable and collision-free direction to avoid encountering danger or collisions while approaching the ultimate goal. The low-level policy, that similarly implements the designed low-level ST-GANet to implicitly predict the obstacles’ motions, takes the subgoals as short-term guidance through an intrinsic reward incentive to generate primitive actions for the robot. Simulation results demonstrate that SG-HDRL using ST-GANets has better performances compared with state-of-the-art baselines. Furthermore, the proposed SG-HDRL is deployed to an experimental platform based on omnidirectional cars, and experiment results validate the effectiveness and practicability of the proposed SG-HDRL.
Tao Li,Fang Zhang,Hao Liu,Tianle Zhai 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.5
Extensive studies from American and some European stock markets have pointed out that venture capital (VC) has a constructive influence on sustainable developments of their invested companies. In this paper, RBF neural network, principal component analysis, and mean variance analysis are functional and well utilized to test the effects of VCs on enterprises' post-IPO performances in China. The empirical results indicate that venture capitals in China brought undesirable effects to both operating performance and market performance, but performed well in controlling harmful volatility. Therefore, we believe VC in China has a certain positive external but not internal influence on sustainable developments of their invested companies.