Introduction to Thesis Code Implementation Projects The code implementation of this thesis is mainly divided into two independent projects, which are respectively responsible for deep reinforcement learning environment construction and core implementation of the MADDPG algorithm. The specific functions and reference basis of each project are as follows:

1. Project 1: multiagent-particle-envs-master This project is mainly used to build the environment required for deep reinforcement learning experiments. Its design and implementation refer to the paper 《Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments》, with the paper link: https://arxiv.org/pdf/1706.02275.pdf. Core Responsibilities Basic Environment Construction: Define and implement the core interaction module of the agent, including the mathematical modeling and code encapsulation of the State Space and Action Space, to ensure consistency with the multi-agent interaction logic in the paper. Reward Function Design: According to the experimental objectives (such as cooperative, competitive, and mixed scenarios), implement a reward function that meets the task requirements, quantify the effectiveness of the agent's behavior, and provide feedback signals for reinforcement learning training. Expansion of Training Scenarios: Design a variety of experimental scenarios (such as resource collection, cooperative navigation, adversarial games, etc.), verify the generalization ability of the agent under different task modes, and ensure the robustness of the algorithm.
2. Project 2: MADDPG-master Based on the PyTorch framework, this project implements the complete process of the MADDPG (Multi-Agent Deep Deterministic Policy Gradient) algorithm and is the core module for algorithm verification in this thesis. Core Responsibilities Network Structure Construction: Implement the neural network modules required by the MADDPG algorithm, including the Critic Network (evaluating action value) and Actor Network (generating deterministic actions), and complete detailed designs such as network parameter initialization and activation function selection. Model Training Process: Encapsulate the complete training logic, including core steps such as experience replay, soft update of target networks, and gradient descent optimization, to ensure the stability and convergence of the training process. Model Verification Mechanism: Design a verification script, load the trained model, evaluate the decision-making performance of the agent in preset test scenarios, and output key indicators (such as average reward, task completion rate, etc.) to verify the effectiveness of the algorithm.