---
language: "en"
license: "mit"
tags:
  - reinforcement-learning
  - planning-agent
  - natural-language
pipeline_tag: "reinforcement-learning"
---

# 🤖 AUTONOME — Language-to-Action Planning Agent

## 🧭 Overview
**AUTONOME** is a natural-language-driven reinforcement learning agent that interprets textual goals like _"organize my workspace"_ and translates them into executable robotic or API-level action plans.

## ⚙️ Features
- Natural language goal interpretation 🗣️
- Sequential plan generation 🧩
- Reinforcement-based reward optimization 🧠
- Modular design — easy to extend to real robotic APIs 🤖

## 🧩 Project Structure
```
autonome/
├── environment.py         # Simulated environment
├── agent.py               # Neural agent class
├── planner.py             # Converts goals to steps
├── train_agent.py         # Minimal training loop
├── infer_action.py        # Text-to-plan inference
├── app.py                 # Flask API for serving
├── utils.py               # Helper utilities
└── examples/demo_commands.txt
```

## 🚀 Usage
### Training
```bash
python train_agent.py
```
### Inference
```bash
python infer_action.py
```
### API
```bash
python app.py
```
POST to `/plan` with a JSON body:
```json
{ "goal": "organize my workspace" }
```

## 🧠 Example Output
```
Goal: organize my workspace
1. analyze area
2. organize items
3. clean surface
4. relax
```