A method of path planning for unmanned aerial vehicle based on the hybrid of selfish herd optimizer and particle swarm optimizer

Zhao, Ruxin; Wang, Yongli; Xiao, Gang; Liu, Chang; Hu, Peng; Li, Hao

doi:10.1007/s10489-021-02353-y

Cited by 14 publications

(1 citation statement)

References 43 publications

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“…This allows researchers to quickly test and verify RL algorithms in a simulation environment without the need for actual robots or real environments. MuJoCo supports various types of tasks and environmental settings, including robot control, object grasping, and movement [32]; [33,34]. It also allows users to customize the environment as needed to meet various research and application needs.…”

Section: Methodsmentioning

confidence: 99%

Reward shaping using directed graph convolution neural networks for reinforcement learning and games

Sang,

Ahmad Khan,

Yin

et al. 2023

Front. Phys.

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Game theory can employ reinforcement learning algorithms to identify the optimal policy or equilibrium solution. Potential-based reward shaping (PBRS) methods are prevalently used for accelerating reinforcement learning, ensuring the optimal policy remains consistent. Existing PBRS research performs message passing based on graph convolution neural networks (GCNs) to propagate information from rewarding states. However, in an irreversible time-series reinforcement learning problem, undirected graphs will not only mislead message-passing schemes but also lose a distinctive direction structure. In this paper, a novel approach called directed graph convolution neural networks for reward shaping φDCN has been proposed to tackle this problem. The key innovation of φDCN is the extension of spectral-based undirected graph convolution to directed graphs. Messages can be efficiently propagated by leveraging a directed graph Laplacian as a substitute for the state transition matrix. As a consequence, potential-based reward shaping can then be implemented by the propagated messages. The incorporation of temporal dependencies between states makes φDCN more suitable for real-world scenarios than existing potential-based reward shaping methods based on undirected graph convolutional networks. Preliminary experiments demonstrate that the proposed φDCN exhibits a substantial improvement compared to other competing algorithms on both Atari and MuJoCo benchmarks.

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Section: Methodsmentioning

confidence: 99%

Reward shaping using directed graph convolution neural networks for reinforcement learning and games

Sang,

Ahmad Khan,

Yin

et al. 2023

Front. Phys.

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Deep Reinforcement Learning‐Based Air Defense Decision‐Making Using Potential Games

Zhao,

Wang,

et al. 2023

Advanced Intelligent Systems

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This study addresses the challenge of intelligent decision‐making for command‐and‐control systems in air defense combat operations. Current autonomous decision‐making systems suffer from limited rationality and insufficient intelligence during operation processes. Recent studies have proposed methods based on deep reinforcement learning (DRL) to address these issues. However, DRL methods typically face challenges related to weak interpretability, lack of convergence guarantees, and high‐computing power requirements. To address these issues, a novel technique for large‐scale air defense decision‐making by combining a DRL technique with game theory is discussed. The proposed method transforms the target assignment problem into a potential game that provides theoretical guarantees for Nash equilibrium (NE) from a distributed perspective. The air‐defense decision problem is decomposed into separate target selection and target assignment problems. A DRL method is used to solve the target selection problem, while the target assignment problem is translated into a target assignment optimization game. This game is proven to be an exact potential game with theoretical convergence guarantees for an NE. Having simulated the proposed decision‐making method using a digital battlefield environment, the effectiveness of the proposed method is demonstrated.

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Automatic path planning of unmanned combat aerial vehicle based on double-layer coding method with enhanced grey wolf optimizer

Jia

2023

Artif Intell Rev

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A method of path planning for unmanned aerial vehicle based on the hybrid of selfish herd optimizer and particle swarm optimizer

Cited by 14 publications

References 43 publications

Reward shaping using directed graph convolution neural networks for reinforcement learning and games

Reward shaping using directed graph convolution neural networks for reinforcement learning and games

Deep Reinforcement Learning‐Based Air Defense Decision‐Making Using Potential Games

Automatic path planning of unmanned combat aerial vehicle based on double-layer coding method with enhanced grey wolf optimizer

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