# pwhiddy/pokemonredexperiments

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## Links

- GitHub: https://github.com/PWhiddy/PokemonRedExperiments
- awesome-repositories: https://awesome-repositories.com/repository/pwhiddy-pokemonredexperiments.md

## Description

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.

## Tags

### Artificial Intelligence & ML

- [Agentic Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-training-frameworks.md) — Provides a comprehensive framework for training autonomous agents to achieve objectives via environment feedback and trajectory optimization.
- [Reinforcement Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning.md) — Provides a framework for training agents to complete game objectives through trial-and-error reinforcement learning.
- [State-Action-Reward Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/development-runtime-environments/agent-environments/state-action-reward-interfaces.md) — Coordinates the exchange of game states, actions, and rewards to refine navigation policies.
- [Game AI](https://awesome-repositories.com/f/artificial-intelligence-ml/game-ai.md) — Develops intelligent agents capable of completing tasks within a legacy game engine.
- [Game-Based](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/reinforcement-learning-environments/game-based.md) — Implements an autonomous agent trained to complete game objectives via reward optimization in a legacy game environment.
- [Training Monitoring Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/training-monitoring-and-profiling/training-observability-systems/training-monitoring-tools.md) — Provides tools for logging performance metrics and analyzing decision-making during reinforcement learning.
- [Reinforcement Learning Training](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-training.md) — Trains autonomous agents to complete game objectives by optimizing exploration and goal progression. ([source](https://cdn.jsdelivr.net/gh/pwhiddy/pokemonredexperiments@master/README.md))
- [Behavioral Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/behavioral-analysis.md) — Tracks movement patterns and decision-making processes to analyze how AI navigates virtual environments.
- [Training Progress Monitoring](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/utilities/training-progress-monitoring.md) — Streams real-time session data to a global map to visualize movement and area exploration. ([source](https://cdn.jsdelivr.net/gh/pwhiddy/pokemonredexperiments@master/README.md))
- [Training Progress Monitors](https://awesome-repositories.com/f/artificial-intelligence-ml/training-progress-monitors.md) — Ships a dashboard for tracking real-time metrics and session data during the agent training process.

### Data & Databases

- [Game Memory Coordinate Mappings](https://awesome-repositories.com/f/data-databases/data-mapping/coordinate-system-mapping/game-memory-coordinate-mappings.md) — Translates raw game memory addresses into a visual coordinate system to plot agent movement patterns.
- [Memory Mapping Utilities](https://awesome-repositories.com/f/data-databases/memory-mapping-utilities.md) — Reads raw game memory addresses directly to derive entity positions and environmental data.

### Game Development

- [Game Memory Extraction](https://awesome-repositories.com/f/game-development/game-memory-modifiers/game-memory-extraction.md) — Accesses game memory directly to extract entity positions and environmental data for the agent.
- [Memory Address Mapping](https://awesome-repositories.com/f/game-development/game-memory-modifiers/memory-address-mapping.md) — Implements a system to translate raw game memory addresses into visual coordinates for tracking entity positions.
- [Game Memory State Monitoring](https://awesome-repositories.com/f/game-development/game-state-management/game-memory-state-monitoring.md) — Extracts and logs raw memory data from a running game to track agent performance.

### System Administration & Monitoring

- [Agent Trajectory Logs](https://awesome-repositories.com/f/system-administration-monitoring/audit-logs/agent-trajectory-logs.md) — Records and inspects agent trajectories and performance metrics on an external dashboard for analysis.
- [Agent Performance Visualizers](https://awesome-repositories.com/f/system-administration-monitoring/agent-observability/agent-performance-visualizers.md) — Renders agent movements and positions on a map in real-time to analyze navigation patterns. ([source](https://pwhiddy.github.io/pokerl-map-viz/))
- [Model Training Metrics](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/observability-platforms/metric-performance-monitors/model-training-metrics.md) — Logs performance data to external dashboards to monitor training progress and agent behavior. ([source](https://cdn.jsdelivr.net/gh/pwhiddy/pokemonredexperiments@master/README.md))
- [Real-Time Metric Visualization](https://awesome-repositories.com/f/system-administration-monitoring/real-time-metric-visualization.md) — Streams live session data to global maps and dashboards to monitor learning progress.

### Hardware & IoT

- [Agent Trajectory Visualizations](https://awesome-repositories.com/f/hardware-iot/agent-trajectory-visualizations.md) — Streams real-time agent session data to a map to visualize navigation patterns and area exploration.
- [Real-Time Map Visualizations](https://awesome-repositories.com/f/hardware-iot/real-time-map-visualizations.md) — Streams movement data to a map to monitor AI navigation and exploration in real time.