# MrSyee/pg-is-all-you-need

Policy Gradient is all you need! A step-by-step tutorial for well-known PG methods.

Repository: https://github.com/MrSyee/pg-is-all-you-need
Canonical: https://ross.abutalabs.com/products/pg-is-all-you-need
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2026-07-17T22:03:41+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 93, release rhythm 35, longevity 100
- inputs: {"age_days": 2606, "days_push": 47, "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 1038, forks 127 (observed 2026-08-28T04:03:19.691642+00:00)

## What it is
A step-by-step tutorial repository of Jupyter notebooks teaching policy gradient reinforcement learning algorithms from A2C to SAC, with theory and object-oriented implementations. Each chapter runs in Colab and uses OpenAI Gym environments like Pendulum.

## Use cases
- learn policy gradient reinforcement learning algorithms step by step
- understand PPO implementation with theory
- study actor-critic methods like A2C and SAC
- see object-oriented RL algorithm implementations in notebooks
- learn DDPG and TD3 continuous control algorithms
- learn learning from demonstrations like DDPGfD and behavior cloning
- run RL tutorials in Colab without local setup

## When to choose
- you want a guided tutorial covering the main policy gradient family of RL algorithms
- you prefer notebooks combining theory with runnable code
- you want to learn RL from demonstrations and sparse-reward techniques

## When to avoid
- you need a production-ready RL library or framework
- you want DQN/value-based methods (see Rainbow is All You Need instead)
- you need scalable distributed RL training

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, reinforcement-learning
- domain: reinforcement-learning, tutorials, machine-learning
- platform: python, browser
- tags: policy-gradient, jupyter-notebooks, actor-critic, ppo, sac, ddpg, td3, openai-gym, colab

## Member repositories
- MrSyee/pg-is-all-you-need (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:19.691642+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 2026-08-30T07:04:12.989890+00:00, confidence not recorded.
  - readme: https://github.com/MrSyee/pg-is-all-you-need (fetched 2026-08-28T04:03:19.691642+00:00, sha 65220540c628)
- Data as of 2026-08-30T08:39:29.467469+00:00.