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Deep reinforcement learning using least-squares truncated temporal-difference

CAAI Transactions on Intelligence Technology 2024年第2期9卷 425-439页

作者： Junkai Ren Yixing Lan Xin Xu Yichuan Zhang Qiang Fang Yujun Zeng College of Intelligence Science and Technology National University of Defense TechnologyChangshaChina State Key Laboratory of Astronautic Dynamics Xi'an Satellite Control CenterXi'anChina

Policy evaluation(PE)is a critical sub-problem in reinforcement learning,which estimates the value function for a given policy and can be used for policy ***,there still exist some limitations in current PE methods,such as low sample efficiency and local convergence,especially on complex *** this study,a novel PE algorithm called Least-Squares Truncated Temporal-Difference learning(LST2D)is *** LST2D,an adaptive truncation mechanism is designed,which effectively takes advantage of the fast convergence property of Least-Squares Temporal Difference learning and the asymptotic convergence property of Temporal Difference learning(TD).Then,two feature pre-training methods are utilised to improve the approximation ability of ***,an Actor-Critic algorithm based on LST2D and pre-trained feature representations(ACLPF)is proposed,where LST2D is integrated into the critic network to improve learning-prediction *** simulation studies were conducted on four robotic tasks,and the corresponding results illustrate the effectiveness of *** proposed ACLPF algorithm outperformed DQN,ACER and PPO in terms of sample efficiency and stability,which demonstrated that LST2D can be applied to online learning control problems by incorporating it into the actor-critic architecture.

关键词： Deep reinforcement learning policy evaluation temporal difference value function approximation

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Hierarchical fuzzy ART for Q-learning and its application in air combat simulation

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International Journal of Modeling, Simulation, and Scientific Computing 2017年第4期8卷 205-223页

作者： Yanan Zhou Yaofei Ma Xiao Song Guanghong Gong School of Automation Science and Electrical Engineering Beihang University XueYuan Road No.37HaiDian District Beijing 100191P.R.China

value function approximation plays an important role in reinforcement learning(RL)with continuous state space,which is widely used to build decision models in *** traditional approaches require experienced designers to manually specify the formulization of the approximating function,leading to the rigid,non-adaptive representation of the value *** address this problem,a novel Q-value function approximation method named‘Hierarchical fuzzy Adaptive Resonance Theory’(HiART)is proposed in this *** is based on the Fuzzy ART method and is an adaptive classification network that learns to segment the state space by classifying the training input *** begins with a highly generalized structure where the number of the category nodes is limited,which is beneficial to speed up the learning process at the early ***,the network is refined gradually by creating the attached subnetworks,and a layered network structure is formed during this *** on this adaptive structure,HiART alleviates the dependence on expert experience to design the network *** effectiveness and adaptivity of HiART are demonstrated in the Mountain Car benchmark problem with both fast learning speed and low computation ***,a simulation application example of the one versus one air combat decision problem illustrates the applicability of HiART.

关键词： Fuzzy ART Q-learning value function approximation air combat simulation

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