AI RESEARCH PAPER ANALYSIS
Research on Single Target Encirclement Strategy of Multi Intelligent Unmanned Equipment Cluster Based on MADDPG
Authors: Jing Yu, Yaoyao Li, Xiao Chen, Xiaoyan Qin
Publication: 2025 5th International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR 2025)
To solve the problem of intelligent unmanned equipment clusters surrounding single targets, a multi-agent collaborative capture scenario is constructed. The problem was modeled and mathematically described within the reinforcement learning MADDPG framework, clarifying the definition of successful capture in this context. Designed critic network and actor network, defined state transitions and agent behavior actions in continuous action space; The reward function has been designed, taking into account individual and collaborative factors, distance and dispersion factors, etc., to improve the comprehensiveness of the reward function description. Simulation experiments have shown that the model can complete the task of encircling a single target, verifying the feasibility and effectiveness of the method. The experiment also confirmed that the technical parameters of the capture agent and the target agent affect the capture effect; In the comparison of hyperparameters, it was found that different learning rates affect the time and success rate of successful capture.
Executive Impact & Key Findings
This research presents a robust MADDPG-based solution for multi-agent encirclement, offering significant advancements in autonomous system collaboration and efficiency. Key metrics highlight its potential for real-world application in defense and logistics.
0%
Increase in Capture Success Rate
0%
Reduction in Rounds to First Capture
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Cooperating Intelligent Agents
Deep Analysis & Enterprise Applications
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Multi-Agent Deep Deterministic Policy Gradient (MADDPG) Framework
The MADDPG algorithm is an advanced reinforcement learning approach designed for multi-agent environments. It extends DDPG by enabling each agent to have independent Actor-Critic networks while leveraging global information during training for enhanced stability and effectiveness.
Enterprise Process Flow
Agent Observations (Oi)
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Actor Network (πi) Generates Ai
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Collection of All Agent Actions (A1...An)
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Environment State Transition
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Critic Network (O1...On, A1...An)
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Q-Value Calculation
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Policy Updates (Actors & Critics)
Comprehensive Reward Function Design
A critical component of this MADDPG implementation is the carefully crafted reward function. It integrates multiple factors to guide intelligent agents towards successful encirclement, promoting both individual performance and collaborative synergy.
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Maximum One-Time Reward Value for Successful Capture
This value represents the significant positive reinforcement an agent receives upon successfully encircling a target, driving effective learning towards the overall objective.
Experimental Validation and Parameter Impact
Simulation experiments demonstrated the feasibility and effectiveness of the MADDPG model in completing single-target encirclement tasks. The results highlighted that various technical parameters of both capture and target agents significantly influence the capture effect.
Case Study: Tuning for Enhanced Encirclement
Initial experiments (Experiment 1) showed the model could achieve encirclement, albeit with a relatively low success rate (5% at Episode 80). By adjusting key parameters in Experiment 2—specifically, the target agent's maximum speed from 0.15 to 0.1 and the capture radius from 0.5 to 0.8—a notable improvement was observed. The success rate increased to 17% by Episode 80, and successful encirclement was achieved earlier in the training process, validating the impact of parameter tuning on performance.
Learning Rate Optimization for Performance
The study rigorously evaluated the impact of hyperparameters, particularly the learning rate, on the model's performance. Optimal learning rate tuning is crucial for balancing learning speed and overall success.
| Learning Rate | Cumulative Reward | Rounds to First Success | Highest Success Rate |
|---|---|---|---|
| 0.01 | 13.24 | 60 | 5% |
| 0.03 | 289.74 | 60 | 10% |
| 0.05 | 361.86 | 20 | 5% |
The analysis demonstrates that while a higher learning rate (0.05) can significantly accelerate the initial achievement of capture (20 rounds vs. 60), it may lead to underfitting and a lower overall success rate compared to an optimal intermediate value (0.03). This highlights the critical need for fine-tuning hyperparameters to maximize both efficiency and accuracy in real-world deployments.
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Estimated Annual Cost Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
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Discovery & Strategy
Assess current systems, identify high-impact use cases for multi-agent AI, and define clear objectives and KPIs for encirclement or collaborative tasks.
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