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Exposes a trained Deep Q-Network agent for business process resource allocation, enabling natural language interaction with reinforcement learning models. It pr
Exposes a trained Deep Q-Network agent for business process resource allocation, enabling natural language interaction with reinforcement learning models. It provides tools for simulation control, Q-value analysis, and action explainability to make complex decision-making transparent.
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.
pip install -r requirements.txt
Requirements: Python 3.8+, TensorFlow 2.16+
python -m mcp4drl.test_integration
# Windows
run_server.bat
# Linux/Mac
chmod +x run_server.sh
./run_server.sh
Add to claude_desktop_config.json:
{
"mcpServers": {
"mcp4drl": {
"command": "cmd.exe",
"args": ["/c", "C:\\path\\to\\mcp4drl_repo\\run_server.bat"],
"shell": true
}
}
}
| 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 |
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
Environment variables (optional):
MCP4DRL_MODEL_PATH - Path to trained model (.h5)MCP4DRL_EVENT_LOG - Path to XES event logMCP4DRL_TRANSPORT - stdio (default) or ssePart of doctoral dissertation on intelligent automation of business process management. Demonstrates that RL systems can be made transparent through conversational interfaces.
Research prototype.
Добавь это в claude_desktop_config.json и перезапусти Claude Desktop.
{
"mcpServers": {
"mcp4drl": {
"command": "npx",
"args": []
}
}
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