MCP4DRL - Model Context Protocol for Deep Reinforcement Learning

MCP server that exposes a trained Deep Q-Network (DQN) agent for business process resource allocation through conversational interfaces. Makes "black box" RL systems transparent via natural language queries.

Features

• Environment State Queries - View simulation state, waiting/active cases, resources

• Q-Value Analysis - Inspect Q-values for all actions

• Action Recommendations - Get agent's top choice with justification

• Explainability - Detailed explanations of why actions are chosen

• Heuristic Comparison - Compare with FIFO, SPT, EDF, LST baselines

• Simulation Control - Step through episodes, reset, run full episodes

Related MCP server: flowvenue-mcp-server

Installation

pip install -r requirements.txt

Requirements: Python 3.8+, TensorFlow 2.16+

Quick Start

Test locally

python -m mcp4drl.test_integration

Run MCP server

# Windows
run_server.bat

# Linux/Mac
chmod +x run_server.sh
./run_server.sh

Claude Desktop Integration

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "mcp4drl": {
      "command": "cmd.exe",
      "args": ["/c", "C:\\path\\to\\mcp4drl_repo\\run_server.bat"],
      "shell": true
    }
  }
}

Available MCP Tools

Project Structure

mcp4drl_repo/
├── mcp4drl/           # Main Python package
│   ├── core/          # Wrappers (simulator, agent)
│   ├── models/        # Pydantic schemas
│   └── tools/         # MCP tool implementations
├── simprocess/        # Business process simulation engine
├── data/              # Model and event log
└── mcp4drl_server.py  # Standalone launcher

Configuration

Environment variables (optional):

• MCP4DRL_MODEL_PATH - Path to trained model (.h5)

• MCP4DRL_EVENT_LOG - Path to XES event log

• MCP4DRL_TRANSPORT - stdio (default) or sse

Context

Part of doctoral dissertation on intelligent automation of business process management. Demonstrates that RL systems can be made transparent through conversational interfaces.

License

Research prototype.