Ioannis Antonoglou, founding engineer at DeepMind and co-founder of ReflectionAI, has seen the triumphs of reinforcement learning firsthand. From AlphaGo to AlphaZero and MuZero, Ioannis has built the most powerful agents in the world. Ioannis breaks down key moments in AlphaGo's game against Lee Sodol (Moves 37 and 78), the importance of self-play and the impact of scale, reliability, planning and in-context learning as core factors that will unlock the next level of progress in AI.

Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital

Mentioned in this episode:

PPO: Proximal Policy Optimization algorithm developed by DeepMind in game environments. Also used by OpenAI for RLHF in ChatGPT.
MuJoCo: Open source physics engine used to develop PPO
Monte Carlo Tree Search: Heuristic search algorithm used in AlphaGo as well as video compression for YouTube and the self-driving system at Tesla
AlphaZero: The DeepMind model that taught itself from scratch how to master the games of chess, shogi and Go
MuZero: The DeepMind follow up to AlphaZero that mastered games without knowing the rules and able to plan winning strategies in unknown environments
AlphaChem: Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies
DQN: Deep Q-Network, Introduced in 2013 paper, Playing Atari with Deep Reinforcement Learning
AlphaFold: DeepMind model for predicting protein structures for which Demis Hassabis, John Jumper and David Baker won the 2024 Nobel Prize in Chemistry

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