poca-SoccerTwos Open-Source Deep Reinforcement Learning Model - Suitable for Two-Player Soccer Game Scenarios

Poca SoccerTwos

Developed by hishamcse

A deep reinforcement learning model trained with Unity ML-Agents, specifically designed for two-player soccer game scenarios

Molecular Model

TensorBoard

#Two-player Soccer AI #Multi-agent Collaboration #Unity ML-Agents

Downloads 20

Release Time : 6/23/2024

Model Overview

This model is trained using the POCO algorithm and can control virtual soccer players in 2v2 matches, demonstrating the application of multi-agent reinforcement learning in game AI

Model Features

Multi-agent Collaboration

The model can control multiple agents to collaborate and compete in soccer matches

Based on ML-Agents Framework

Developed using Unity's official ML-Agents toolkit, compatible with Unity environments

Browser Demo

Supports watching the agents' performance directly in a browser

Model Capabilities

Soccer Game Control

Multi-agent Collaboration

Real-time Decision Making

Adversarial Learning

Use Cases

Game AI

Soccer Game NPC

Participates in 2v2 soccer matches as non-player characters

Capable of basic soccer tactics and collaboration

Reinforcement Learning Research

Multi-agent Collaboration Research

Used to study collaborative and competitive behaviors in multi-agent systems

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Poca SoccerTwos

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 ML-Agents: Poca Agent Playing SoccerTwos

🚀 Quick Start

🔗 Codes

📚 Documentation

💻 Usage Examples

Resume the training

Watch your Agent play