🎯 Key Takeaways for quick navigation: 00:41 🧠 AlphaGo, the Go-playing AI, learns from human experts by analyzing prior games and then plays millions of games against itself using reinforcement learning to improve. 02:25 🤖 A policy neural network is trained to predict good moves based on the state of the Go board. 03:41 🌐 The value function estimates the likelihood of winning from a given state, helping the AI plan ahead and make strategic moves. 06:10 🔄 AlphaGo uses reinforcement learning to refine its move policy and value estimation through self-play, simulating millions of games. 07:51 🤯 AlphaZero, a newer approach, relies solely on reinforcement learning and is even more advanced, eliminating the need for learning from human experts. Made with HARPA AI
Thank you for these rather clear explanations!
Fascinating! I wonder what would happen if AlphaZero played on a larger board
🎯 Key Takeaways for quick navigation:
00:41 🧠 AlphaGo, the Go-playing AI, learns from human experts by analyzing prior games and then plays millions of games against itself using reinforcement learning to improve.
02:25 🤖 A policy neural network is trained to predict good moves based on the state of the Go board.
03:41 🌐 The value function estimates the likelihood of winning from a given state, helping the AI plan ahead and make strategic moves.
06:10 🔄 AlphaGo uses reinforcement learning to refine its move policy and value estimation through self-play, simulating millions of games.
07:51 🤯 AlphaZero, a newer approach, relies solely on reinforcement learning and is even more advanced, eliminating the need for learning from human experts.
Made with HARPA AI