# louisnino/RLcode

Repository: https://github.com/louisnino/RLcode
Canonical: https://ross.abutalabs.com/products/rlcode
Language: Python
License Family: other
Last push: 2023-01-29T04:20:38+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": 2375, "days_push": 1312, "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 1062, forks 309 (observed 2026-08-28T04:03:26.169448+00:00)

## What it is
A Chinese-language tutorial series ('白话强化学习') teaching reinforcement learning from intuition-first explanations with accompanying annotated Python/TensorFlow 2.0 code. It covers concepts like Markov chains, Q/V values, TD and Monte Carlo methods, plus algorithms including Q-learning, DQN variants, Policy Gradient, Actor-Critic, A3C, DDPG, TD3, SAC, PPO, and DPPO.

## Use cases
- learn reinforcement learning from scratch without heavy math
- understand Q-learning and DQN with code examples
- implement PPO and actor-critic algorithms in TensorFlow 2
- find a structured learning path for RL algorithms
- get annotated implementations of RL algorithms
- build intuition for Markov chains and Q/V values
- study DDPG, TD3, and SAC algorithm implementations

## When to choose
- you are a beginner wanting intuition-first RL explanations with runnable code
- you prefer Chinese-language tutorials
- you want TensorFlow 2.0 implementations of classic RL algorithms

## When to avoid
- you need production-grade or maintained RL libraries
- you want rigorous mathematical derivations
- you need PyTorch-based examples or recent algorithm coverage

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, reinforcement-learning, developer-tools
- domain: reinforcement-learning, machine-learning, tutorials, education
- platform: python, cross-platform
- tags: reinforcement-learning, tutorial-series, tensorflow2, q-learning, dqn, ppo, actor-critic, chinese-language, zhihu-articles

## Member repositories
- louisnino/RLcode (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:26.169448+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 2026-08-30T06:56:45.373085+00:00, confidence not recorded.
  - readme: https://github.com/louisnino/RLcode (fetched 2026-08-28T04:03:26.169448+00:00, sha 132f112f9737)
- Data as of 2026-08-30T08:39:29.467469+00:00.