alphago

Summary of David Silver's Reinforcement Learning Lecture 8 on integrating learning and planning, covering model-based RL and AlphaGo's use of policy and value networks with Monte Carlo Tree Search.

Eric Jang announces he has been working on a from-scratch implementation of AlphaGo, the 2016 AI breakthrough that inspired him to enter deep learning.

A thread sharing a video of self-play RL training with lidar and PPO in Unity, followed by a lecture on building AlphaGo from scratch.

Recommend a Chinese-subtitled tutorial on building AlphaGo from scratch, suitable for learning AI and reinforcement learning.

A detailed discussion on reinforcement learning and its connection to modern AI, using the reconstruction of AlphaGo with modern tools as a clear example of search and self-play. Key takeaways include neural network amortization of search, credit assignment challenges in LLMs vs AlphaGo, and implications for automated research.

Eric Jang releases AutoGo, a from-scratch tutorial for implementing AlphaGo, including code and a playable bot, demonstrating that frontier capabilities can now be replicated affordably.

A blackboard lecture by Eric Jang walks through building AlphaGo from scratch with modern AI tools, covering RL, MCTS, self-play, and connecting to LLM training, along with a discussion on automated AI research.

Opinion piece arguing that AlphaGo and ChatGPT are the two most significant AI breakthroughs, with ChatGPT having the greatest everyday impact by making AI accessible to the masses.

DeepMind reflects on the 10th anniversary of AlphaGo, highlighting its role in kickstarting the modern AI era and its subsequent impact on scientific research and the pursuit of AGI.

This article reviews the history of AlphaGo's historic victory over Lee Sedol in 2016, analyzes its technical principles combining deep learning and search, and explores the profound impact of this event on the development of AI.