PokemonRedExperiments | Awesome Repository

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.

Features

Agentic Training Frameworks - Provides a comprehensive framework for training autonomous agents to achieve objectives via environment feedback and trajectory optimization.
Reinforcement Learning - Provides a framework for training agents to complete game objectives through trial-and-error reinforcement learning.
State-Action-Reward Interfaces - Coordinates the exchange of game states, actions, and rewards to refine navigation policies.
Game AI - Develops intelligent agents capable of completing tasks within a legacy game engine.

Features

Agentic Training Frameworks - Provides a comprehensive framework for training autonomous agents to achieve objectives via environment feedback and trajectory optimization.
Reinforcement Learning - Provides a framework for training agents to complete game objectives through trial-and-error reinforcement learning.
State-Action-Reward Interfaces - Coordinates the exchange of game states, actions, and rewards to refine navigation policies.
Game AI - Develops intelligent agents capable of completing tasks within a legacy game engine.