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

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1038 stars · 127 forks observed · 2026-08-28

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

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

learning-resource · maturity stable

machine-learning reinforcement-learning reinforcement-learning tutorials machine-learning python browser policy-gradient jupyter-notebooks actor-critic ppo sac ddpg td3 openai-gym colab

Member repositories

For agents

markdown · JSON · MCP: product_card(name="MrSyee/pg-is-all-you-need")