# ucla-rlcourse/RLexample

Some basic examples of playing with RL

Repository: https://github.com/ucla-rlcourse/RLexample
Canonical: https://ross.abutalabs.com/products/rlexample
Language: Jupyter Notebook
License Family: other
Last push: 2026-02-18T05:27:55+00:00

## Health v2 (maintenance only)
Score: 63/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 68, release rhythm 35, longevity 100
- inputs: {"age_days": 2793, "days_push": 196, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1272, forks 301 (observed 2026-08-28T04:04:12.104238+00:00)

## What it is
A collection of Jupyter Notebook and Python script examples demonstrating basic reinforcement learning with Gymnasium, PyTorch, and simulators like AI2THOR, MetaDrive, and Genesis. It serves as a hands-on tutorial companion for UCLA's RL course.

## Use cases
- learn reinforcement learning basics with cartpole examples
- implement a policy gradient agent to play pong
- train a PPO agent to control a car or quadruped robot
- get started with gymnasium environments
- find example code for random and learning agents in RL environments

## When to choose
- you are a student or beginner learning reinforcement learning fundamentals
- you want small, runnable example scripts for Gymnasium and PyTorch RL
- you need starter code for policy gradient or PPO experiments

## When to avoid
- you need a production-ready RL library or framework
- you require a maintained, licensed dependency for a real project
- you want comprehensive, well-tested RL algorithms

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: reinforcement-learning, machine-learning, simulation
- domain: reinforcement-learning, machine-learning, tutorials, education
- platform: python, cross-platform
- tags: gymnasium, pytorch, jupyter-notebooks, policy-gradient, ppo, educational-examples

## Member repositories
- ucla-rlcourse/RLexample (main) score 63

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:12.104238+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T05:03:19.519504+00:00, confidence not recorded.
  - readme: https://github.com/ucla-rlcourse/RLexample (fetched 2026-08-28T04:04:12.104238+00:00, sha 296d8adff0d0)
- Data as of 2026-08-30T08:39:29.467469+00:00.