# 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
## Installation
```bash
pip install -r requirements.txt
```
**Requirements:** Python 3.8+, TensorFlow 2.16+
## Quick Start
### Test locally
```bash
python -m mcp4drl.test_integration
```
### Run MCP server
```bash
# Windows
run_server.bat
# Linux/Mac
chmod +x run_server.sh
./run_server.sh
```
## Claude Desktop Integration
Add to `claude_desktop_config.json`:
```json
{
"mcpServers": {
"mcp4drl": {
"command": "cmd.exe",
"args": ["/c", "C:\\path\\to\\mcp4drl_repo\\run_server.bat"],
"shell": true
}
}
}
```
## Available MCP Tools
| Tool | Description |
|------|-------------|
| `get_environment_state` | Current simulation state |
| `get_eligible_actions` | All possible actions with validity |
| `get_q_values` | Q-values for all actions |
| `get_recommended_action` | Agent's best action |
| `explain_action` | Detailed action explanation |
| `compare_with_heuristic` | Compare with FIFO/SPT/EDF/LST |
| `step_simulation` | Execute one step |
| `reset_simulation` | Reset to initial state |
| `run_episode` | Run full episode with policy |
## 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.
