louisnino/RLcode resource

Health v2 · maintenance only

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Adoption not part of the score

1062 stars · 309 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

learning-resource · maturity maintenance

machine-learning reinforcement-learning developer-tools reinforcement-learning machine-learning tutorials education python cross-platform reinforcement-learning tutorial-series tensorflow2 q-learning dqn ppo actor-critic chinese-language zhihu-articles

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