Developing AI Agents for Hazard‐Aware Game Environments Using Reinforcement Learning

Abstract

This project is Unity-based multi-agent using reinforcement learning environment featuring a 2 players. The primary agent and a spider-agent, is trained using ML-Agents to chase prey agent within a 3D arena filled with static obstacles, collectible coins, and hazards like poison food. The environment is designed to challenge agents with navigation complexity, partial observations, and decision-making under pressure. The project highlights the potential of reinforcement learning in game AI and showcases Unity as a powerful platform for simulating complex multi-agent scenarios.