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Updated: Jul 23, 2025

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A Multiphase Semistatic Training Method for Swarm Confrontation Using Multiagent Deep Reinforcement Learning.

He Cai1, Yaoguo Luo1, Huanli Gao1

  • 1School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China.

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This study introduces a multiphase semistatic training method for multi-agent deep reinforcement learning (MDRL) in swarm confrontation. This approach enhances training efficiency, enabling weaker agents to learn from stronger ones more effectively.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Robotics

Background:

  • Swarm confrontation requires effective multi-agent training strategies.
  • Traditional single-phase training can be inefficient for complex scenarios.
  • Reinforcement learning offers a framework for agent training.

Purpose of the Study:

  • To propose and evaluate a novel multiphase semistatic training method for swarm confrontation.
  • To improve the training efficiency and learning outcomes for agents in competitive environments.
  • To address the challenge of weak agents failing to learn in traditional training paradigms.

Main Methods:

  • Development of a 3V3 tank fight game simulation using the Unity platform.
  • Implementation of the Multi-Agent Proximal Policy Optimization with Curriculum Adaptation (MA-POCA) algorithm from the ML-Agent toolkit.
  • Application of a multiphase learning strategy with incrementally increasing performance levels for strong agents.
  • Incorporation of semistatic learning where strong agents pause learning against weaker opponents.

Main Results:

  • The proposed multiphase semistatic training method significantly increases training efficiency compared to single-phase methods.
  • Experimental results demonstrate improved learning outcomes for weaker agents.
  • The method reduces the computational cost and time required for effective agent training.

Conclusions:

  • The multiphase semistatic training method is an effective approach for swarm confrontation in multi-agent deep reinforcement learning.
  • This strategy facilitates efficient knowledge transfer from strong to weak agents.
  • The findings offer valuable insights for developing more capable and adaptable AI agents in competitive environments.