AgentCrafter: Advanced Software Modeling and Design Project

Welcome to AgentCrafter, a comprehensive framework for Reinforcement Learning (RL) with advanced multi-agent capabilities and experimental Large Language Model (LLM) integration.

Development Roadmap

This project was developed following a structured, incremental approach that demonstrates methodical progression from basic RL concepts to advanced multi-agent systems:

Foundation Phase

1. Grid Q-Learning - Core reinforcement learning implementation
2. Visual Q-Learning - Enhanced visualization and user experience
3. First DSL Version - Domain-specific language foundation

Advanced Features Phase

1. MARL Extension - Multi-agent reinforcement learning capabilities
2. DSL Adaptation - Enhanced DSL for multi-agent scenarios
3. QTable LLM - AI-powered Q-table generation
4. Wall LLM - AI-powered environment design

Testing and Validation Phase

The following tests were developed, before, during, and after development incrementally:

1. Unit Tests - Comprehensive testing framework
2. Gherkin/Cucumber Tests - Behavior-driven development testing
3. ScalaCheck Tests - Property-based testing framework

This roadmap explicitly shows how each component builds upon previous work, creating a robust foundation for complex multi-agent reinforcement learning scenarios.

Documentation Structure

The documentation follows the development journey, explaining how each component works:

Q-Learning

Foundation Analysis and Implementation

• Analysis of core learning algorithms and grid-based environments
• Grid Q-Learning: basic implementation and environment dynamics
• Visual Q-Learning: enhanced user experience and visualization
• First DSL version: design decisions and syntax foundation

This section establishes the fundamental concepts and architecture that support all advanced features.

MARL

Multi-Agent Extensions and Coordination

• What extends beyond basic Q-Learning: coordination mechanisms and shared environments
• Multi-agent implementation: Many QTable, agent coordination and state management
• DSL additions: enhanced syntax for multi-agent scenarios, triggers, and complex configurations

This section demonstrates how the foundation scales to support multiple coordinated agents in complex environments.

LLM

AI-Powered Enhancement Features

• LLM Q-Learning: AI-generated Q-tables for intelligent initialization
• Wall LLM: natural language environment design and generation
• Prompt engineering: how prompts are structured and data is processed

This section covers experimental AI integration, including both successes and limitations.

Grammar

Complete DSL Specification Comprehensive syntax reference and language specification.

Conclusions

Project Outcomes and Insights

• MARL Success: Why multi-agent coordination works effectively
• LLM Limitations: Why AI integration faces significant challenges and what this teaches us

Key Results Summary

✅ MARL Works Effectively

• Reliable multi-agent coordination in complex environments
• Seamless DSL integration with advanced features

⚠️ LLM Integration Has Limitations

• Technical integration succeeds but practical benefits are limited
• LLMs struggle to understand information in a spatial environment with multiple entities interacting within it
• Traditional algorithmic approaches often provide better reliability

Getting Started

To understand the complete development journey:

1. Q-Learning - Understand the foundational architecture and core concepts
2. MARL - See how multi-agent features build naturally on the foundation
3. LLM - Explore experimental AI features and their real-world limitations
4. Grammar - Reference the complete DSL specification
5. Conclusions - Learn from project outcomes and insights