kyegomez/OpioidRL

OpioidRL is a cutting-edge reinforcement learning (RL) library that simulates drug addiction behaviors within RL agents. Inspired by the addictive properties of drugs like methamphetamine and crack cocaine, OpioidRL offers a unique environment where agents experience reward dependency, high-risk decision-making, and compulsive behaviors — pushing

35
/ 100
Emerging

The library provides specialized OpenAI Gym-compatible environments (Crack-v0, Meth-v0) that model distinct addiction mechanics—tolerance escalation, withdrawal penalties, and relapse probability—all tunable via configuration parameters. It integrates directly with standard RL frameworks like Stable Baselines3, PyTorch, and TensorFlow through a standardized API, enabling seamless experimentation with addiction-induced behavioral patterns across existing training pipelines.

Available on PyPI.

Maintenance 13 / 25
Adoption 4 / 25
Maturity 18 / 25
Community 0 / 25

How are scores calculated?

Stars

8

Forks

Language

Python

License

MIT

Last pushed

Mar 09, 2026

Commits (30d)

0

Dependencies

7

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/agents/kyegomez/OpioidRL"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.