3D Soccer Sim

3D Soccer Sim is a Unity-based soccer simulation environment designed for experimenting with multi-agent reinforcement learning in a 2v2 setting. The simulator focuses on training two deep reinforcement learning agents to play soccer cooperatively and competitively using real-time spatial data and game dynamics. This project was developed as an assesment for Deep learning and Multi-agent systems Courses in University of Tehran.

Built with Unity and C#, this project provides an interactive and extensible framework for AI training, benchmarking, and visualization in multi-agent systems.

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Screenshots:

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Project Goals

• Build a 3D soccer simulation environment in Unity
• Implement a 2 vs 2 multi-agent system
• Train agents using deep reinforcement learning techniques
• Simulate realistic interactions between agents, ball, and environment
• Observe emergent cooperative and adversarial behaviors between agents

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Tech Stack

Component	Technology
Game Engine	Unity (C#)
AI Framework	Custom integration with ML libraries
Learning Algorithms	PPO
Agents	Multi-Agent System (2v2)
Simulation Target	Soccer Environment

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Features

• Supports training and evaluation of AI agents in Unity
• Real-time soccer mechanics with ball control.
• Easily extendable for reward shaping, new strategies, or larger teams
• Visualization tools for debugging and training observation

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Use Cases

• Research on multi-agent reinforcement learning in dynamic environments
• Simulation-based AI development and policy testing
• Teaching tool for ML concepts in interactive 3D settings
• Benchmarking multi-agent coordination and competition strategies

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Reinforcement Learning Setup

• Agents receive input from the environment (positions, velocities, ball state, field of view)
• Actions include move, turning around
• Rewards based on goals, positioning, and strategy
• Supports integration with frameworks like ML-Agents, PyTorch, or TensorFlow via socket or REST interface