# MorvanZhou/Reinforcement-learning-with-tensorflow

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

Repository: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow
Canonical: https://ross.abutalabs.com/products/reinforcement-learning-with-tensorflow
Homepage: https://mofanpy.com/tutorials/machine-learning/reinforcement-learning/
Language: Python
License: MIT
License Family: permissive
Topics: reinforcement-learning, tutorial, q-learning, sarsa, sarsa-lambda, deep-q-network, a3c, ddpg, policy-gradient, dqn, double-dqn, prioritized-replay, dueling-dqn, deep-deterministic-policy-gradient, asynchronous-advantage-actor-critic, actor-critic, tensorflow-tutorials, proximal-policy-optimization, ppo, machine-learning
Last push: 2024-03-31T05:40:51+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3406, "days_push": 885, "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 9506, forks 4983 (observed 2026-08-28T04:10:31.545615+00:00)

## What it is
A collection of simple reinforcement learning tutorial code in Python/TensorFlow, accompanying the Morvan Python (莫烦Python) video course in Chinese and English. It covers algorithms from Q-learning and Sarsa through DQN variants, policy gradients, Actor-Critic, DDPG, A3C, PPO, and curiosity-driven models.

## Use cases
- learn reinforcement learning from scratch with code examples
- understand how DQN and its variants work
- implement policy gradient and actor-critic methods
- find PPO and DDPG reference implementations
- follow a video tutorial series on RL algorithms
- practice RL with OpenAI Gym environments

## When to choose
- you are a beginner wanting step-by-step RL tutorials with runnable code
- you prefer video-based learning in Chinese or English
- you want minimal, readable implementations of classic RL algorithms

## When to avoid
- you need production-ready or well-maintained RL libraries
- you want modern frameworks like RLlib, Stable-Baselines3, or CleanRL
- you need up-to-date TensorFlow 2.x or PyTorch code

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: reinforcement-learning, deep-learning, machine-learning
- domain: reinforcement-learning, machine-learning, tutorials, deep-learning
- platform: python, cross-platform
- tags: q-learning, dqn, policy-gradient, actor-critic, ddpg, a3c, ppo, openai-gym, tensorflow, tutorials, chinese-language

## Member repositories
- MorvanZhou/Reinforcement-learning-with-tensorflow (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:31.545615+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 2026-08-29T17:22:00.992366+00:00, confidence not recorded.
  - readme: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow (fetched 2026-08-28T04:10:31.545615+00:00, sha 9391ccc5e9ed)
  - homepage: https://mofanpy.com/tutorials/machine-learning/reinforcement-learning/ (fetched 2026-08-29T08:21:55.197165+00:00, sha c2ebaf805c0a)
  - site_page: https://mofanpy.com/about (fetched 2026-08-29T08:21:55.206352+00:00, sha 828465c2b53d)
- Data as of 2026-08-30T08:39:29.467469+00:00.