This project is a game AI training framework designed to develop and monitor reinforcement learning agents within a legacy game environment. It functions as a training and monitoring system that optimizes autonomous agents to complete game objectives through exploration and reward-based learning.
The framework includes tools for game memory mapping and real-time trajectory visualization. These capabilities translate raw game memory addresses into visual coordinates, allowing agent movements and session data to be streamed to a map for the analysis of navigation patterns and area exploration.
The system covers game state monitoring and agent behavior analysis by logging performance metrics to external dashboards. This enables the tracking of decision-making processes and environmental variables throughout the reinforcement learning cycle.