Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel auto...
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An alarm flood is a situation where the alarm rate is too high and exceeds the operator's management ability. The occurrence and propagation of an alarm flood cause difficulties for the operators to respond to cri...
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In practical applications of multi-agent systems, agents are often heterogeneous, and each type of them typically has different task objectives. For heterogeneous multi-agent reinforcement learning(HMARL), the diversi...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
In practical applications of multi-agent systems, agents are often heterogeneous, and each type of them typically has different task objectives. For heterogeneous multi-agent reinforcement learning(HMARL), the diversity of agent types and the unbalanced agent number of each type can lead to the curse of dimensionality and non-stationary. Moreover, the increase in the number of heterogeneous agents may result in slow convergence during training. This paper proposes a graph-based selection-activation reinforcement learning(GSARL) method for training heterogenous multi-agent collaboration strategies. It first constructs agents based on their types, then extracts the global adjacency matrices and the ally adjacency matrices from the agents' observations, and calculates the global feature matrices. Afterwards, GSARL utilizes hierarchical graph convolutional network to sequentially convolve the global information and ally information, obtaining action logits based on agent types. By using the neural topology graph and the selection-activation method, the optimal multi-agent collaboration configuration is obtained through combinatorial optimization. Experiments are conducted in an adversarial combat simulation environment involving collaborative Unmanned Aerial Vehicles(UAVs) and Unmanned Ground Vehicles(UGVs). Simulation results show that the proposed method can accelerate convergence while allowing that each type of heterogeneous agents can leverage its unique advantages.
This paper focuses on the problem of stability analysis for Takagi-Sugeno systems with time-varying delays. Firstly, a suitable Lyapunov-Krasovskii functional (LKF) containing fuzzy line-integral Lyapunov functional i...
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With the rapid development of deep learning, it has been widely applied in fields such as computer vision, natural language processing, and robotics. Despite the superior performance of deep learning in object detecti...
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In this paper, the stability of Amplidyne Electrical systems (AESs) with a time-varying delay is studied. Firstly, the model of AESs with a time-varying delay is established. Secondly, an augmented Lyapunov-Krasovskii...
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A fault diagnosis method based on Discrete Hidden Markov Models is proposed in this paper to identify the fault causing alarm flood sequences. The proposed method consists of the following steps: First, the alarm floo...
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A new Gaussian approximate (GA) filter for nonlinear systems with one-step randomly delayed measurement and correlated noise is proposed in this paper. Firstly, a general framework of Gaussian filter is designed under...
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In this paper, the master-slave synchronization issue of chaotic Lur' e systems with time-varying-delay feedback control is investigated. Firstly, the synchronization problem of chaotic system is transformed into ...
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Since landslide is one of the most universal natural disasters in China, the study of regional landslide susceptibility evaluation is important to protect people's lives and property. This paper analyzes the geosp...
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