README.md

metadata

title: Smart Factory Scheduling Environment
emoji: 🏭
colorFrom: blue
colorTo: green
sdk: docker
tags:
  - openenv
  - reinforcement-learning
  - scheduling
pinned: false

Smart Factory Scheduling Environment

An OpenEnv-compliant RL environment simulating real-world industrial scheduling: assign jobs to machines, handle breakdowns, and maximise throughput within deadlines.

Observation Space

Field	Type	Description
machines	List[Machine]	id, status (idle/busy/broken), current_job, failure_rate
pending_jobs	List[Job]	id, remaining_time, deadline, priority (1-3), assigned_machine
completed_jobs	List[Job]	Jobs finished this episode
time	int	Current time step
max_steps	int	Episode length
done	bool	Episode terminated
reward	float	Reward from last action

Action Space

Action	Effect
assign_job <job_id> <machine_id>	Assign pending job to idle machine
repair <machine_id>	Restore broken machine to idle
wait	Advance time with no change

Reward Function

Event	Reward
Job completed on time	+1.00 + 0.20 × priority
Job completed late	+0.30
Valid assignment	+0.10
Invalid action	−0.10
Idle machine (pending jobs exist)	−0.05 per machine
Job past deadline	−0.10 per step
Repair broken machine	+0.05

Tasks

Task	Machines	Jobs	Failure Rate	Max Steps	Baseline Score
easy	2	3	0%	20	1.000
medium	4	7	8%	30	~0.557
hard	6	12	15%	40	~0.457

Score formula: 0.5 × completion_rate + 0.3 × on_time_rate + 0.2 × utilization_bonus

Setup

pip install -r requirements.txt

Run HTTP Server (HF Space)

python server.py
# Routes: GET /health  POST /reset  POST /step  GET /state  GET /schema

Run Inference (LLM agent)

export OPENAI_API_KEY=<your-key>
export FACTORY_TASK=easy   # easy | medium | hard
python inference.py

Run RL Training

python train.py --task easy --episodes 10 --provider openai
python train.py --task medium --episodes 10 --provider claude

Interactive Demo

python app.py   # opens at http://localhost:7860

Docker

docker build -t factory-env .
docker run -e OPENAI_API_KEY=<key> -e FACTORY_TASK=easy -p 7860:7860 factory-env

Baseline Scores

Task	Score	Steps
easy	1.000	4
medium	~0.529	12
hard	~0.533	34

Project Structure

├── factory_env/
│   ├── env.py       # FactoryEnv (openenv.core.Environment)
│   ├── models.py    # FactoryAction, FactoryObservation, FactoryState
│   ├── tasks.py     # Task configurations
│   └── grader.py    # Score computation
├── inference.py     # LLM baseline agent
├── train.py         # Multi-episode RL training loop
├── server.py        # FastAPI HTTP server for HF Space
├── app.py           # Gradio interactive demo
├── openenv.yaml     # OpenEnv metadata
└── Dockerfile