# ericyangyu/PPO-for-Beginners

A simple and well styled PPO implementation. Based on my Medium series: https://medium.com/@eyyu/coding-ppo-from-scratch-with-pytorch-part-1-4-613dfc1b14c8.

Repository: https://github.com/ericyangyu/PPO-for-Beginners
Canonical: https://ross.abutalabs.com/products/ppo-for-beginners
Language: Python
License: MIT
License Family: permissive
Topics: ppo, reinforcement-learning, reinforcement-learning-algorithms, machine-learning, pytorch
Last push: 2024-10-01T02:26:43+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2190, "days_push": 702, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1268, forks 158 (observed 2026-08-28T04:04:11.525192+00:00)

## What it is
A minimal, well-documented PyTorch implementation of Proximal Policy Optimization (PPO) designed as a companion to a Medium tutorial series for reinforcement learning beginners. It follows OpenAI Spinning Up pseudocode with clearly annotated algorithm steps and assumes continuous observation and action spaces.

## Use cases
- learn how to implement PPO from scratch in PyTorch
- understand policy gradient algorithms with readable code
- follow a step-by-step PPO tutorial series
- study a clean reference PPO implementation without fancy tricks
- train and test a PPO agent on a continuous control environment
- adapt a simple PPO baseline for my own RL experiments

## When to choose
- you are new to reinforcement learning and want a readable PPO walkthrough
- you want code mapped line-by-line to Spinning Up pseudocode
- you prefer a bare-bones implementation over production RL frameworks
- you want to learn by modifying a small, well-styled codebase

## When to avoid
- you need a production-grade, high-performance RL library
- you need discrete action spaces out of the box
- you want vectorized environments, distributed training, or many built-in tricks
- you need a maintained library with frequent updates and broad environment support

## Facets
- artifact type: learning-resource
- maturity: stable
- function: reinforcement-learning, machine-learning
- domain: reinforcement-learning, machine-learning, tutorials
- platform: python, cross-platform
- tags: ppo, pytorch, policy-gradient, educational, tutorial-series, beginner-friendly

## Member repositories
- ericyangyu/PPO-for-Beginners (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:11.525192+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 2026-08-30T05:03:37.714687+00:00, confidence not recorded.
  - readme: https://github.com/ericyangyu/PPO-for-Beginners (fetched 2026-08-28T04:04:11.525192+00:00, sha 741bbe3d7eab)
- Data as of 2026-08-30T08:39:29.467469+00:00.