Forward-Looking Imaginative Planning Framework Combined with Prioritized-Replay Double DQN

Liu, Bingyan; Ye, Xiongbing; Gao, Yong; Dong, Xianzhou; Wang, Xinbo; Liu, Biliu

doi:10.1109/iccar.2019.8813352

Cited by 6 publications

(1 citation statement)

References 3 publications

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“…It has been widely used in computer transportation and other fields, achieved remarkable results [23]. Although these studies of Yin C [5], Liu B Y [22], Wu X G [42], et al make Deep Q Networks be widely used in the field of control decision. Deep Q Networks still faces a similar problem to table Q-learning in continuous space applications, that is, the number of operations that need to be explicitly expressed increases exponentially with the increase of operational dimensions.…”

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confidence: 99%

Branching improved Deep Q Networks for solving pursuit-evasion strategy solution of spacecraft

Bingyan¹,

Ye²,

Dong³

et al. 2022

JIMO

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<p style='text-indent:20px;'>With the continuous development of space rendezvous technology, more and more attention has been paid to the study of spacecraft orbital pursuit-evasion differential game. Therefore, we propose a pursuit-evasion game algorithm based on branching improved Deep Q Networks to obtain a space rendezvous strategy with non-cooperative target. Firstly, we transform the optimal control of space rendezvous between spacecraft and non-cooperative target into a survivable differential game problem. Next, in order to solve this game problem, we construct Nash equilibrium strategy and test its existence and uniqueness. Then, in order to avoid the dimensional disaster of Deep Q Networks in the continuous behavior space, we construct a TSK fuzzy inference model to represent the continuous space. Finally, in order to solve the complex and timeconsuming self-learning problem of discrete action sets, we improve Deep Q Networks algorithm, and propose a branching architecture with multiple groups of parallel neural Networks and shared decision modules. The simulation results show that the algorithm achieves the combination of optimal control and game theory, and further improves the learning ability of discrete behaviors. The algorithm has the comparative advantage of continuous space behavior decision, can effectively deal with the continuous space chase game problem, and provides a new idea for the solution of spacecraft orbit pursuit-evasion strategy.</p>

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confidence: 99%

Branching improved Deep Q Networks for solving pursuit-evasion strategy solution of spacecraft

Bingyan¹,

Ye²,

Dong³

et al. 2022

JIMO

Self Cite

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Deep Reinforcement Learning-Based Intelligent Decision-Making for Orbital Game of Satellite Swarm

Yu,

Yue,

Huang

et al. 2023

Mechanisms and Machine Science

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Replay in minds and machines

Wittkuhn

Chien

Hall-McMaster

2021

Neuroscience & Biobehavioral Reviews

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Experience-related brain activity patterns reactivate during sleep, wakeful rest, and brief pauses from active behavior. In parallel, machine learning research has found that experience replay can lead to substantial performance improvements in artificial agents. Together, these lines of research suggest replay has a variety of computational benefits for decision-making and learning. Here, we provide an overview of putative computational functions of replay as suggested by machine learning and neuroscientific research. We show that replay can lead to faster learning, less forgetting, reorganization or augmentation of experiences, and support planning and generalization. In addition, we highlight the benefits of reactivating abstracted internal representations rather than veridical memories, and discuss how replay could provide a mechanism to build internal representations that improve learning and decision-making.

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Forward-Looking Imaginative Planning Framework Combined with Prioritized-Replay Double DQN

Cited by 6 publications

References 3 publications

Branching improved Deep Q Networks for solving pursuit-evasion strategy solution of spacecraft

Branching improved Deep Q Networks for solving pursuit-evasion strategy solution of spacecraft

Deep Reinforcement Learning-Based Intelligent Decision-Making for Orbital Game of Satellite Swarm

Replay in minds and machines

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