Chapter
Self-Play for Game Optimization
17:21 - 22:46 (05:25)
Self-play allows for perfect information game optimization through generalization of past experiences, while algorithms can start from random play and learn through continuous self-play.
Clips
The possibilities of creating drama and chaos through AI in video games is an exciting concept for human connection and emotional exploration.
17:21 - 18:31 (01:10)
Summary
The possibilities of creating drama and chaos through AI in video games is an exciting concept for human connection and emotional exploration. With the understanding that the video game world is not real, there is more flexibility to create messy situations and make mistakes.
ChapterSelf-Play for Game Optimization
Episode#344 – Noam Brown: AI vs Humans in Poker and Games of Strategic Negotiation
PodcastLex Fridman Podcast
By picking actions that have higher regret with higher probability, algorithms can learn how to play games and converge to Nash equilibrium.
18:31 - 20:25 (01:54)
Summary
By picking actions that have higher regret with higher probability, algorithms can learn how to play games and converge to Nash equilibrium. An example is given with AI learning how to play poker.
ChapterSelf-Play for Game Optimization
Episode#344 – Noam Brown: AI vs Humans in Poker and Games of Strategic Negotiation
PodcastLex Fridman Podcast
Self-play in game theory involves a player improving their strategy by playing against a copy of itself, while generalization involves applying knowledge from past experiences to new, similar situations.
20:25 - 22:46 (02:20)
Summary
Self-play in game theory involves a player improving their strategy by playing against a copy of itself, while generalization involves applying knowledge from past experiences to new, similar situations. Both of these techniques rely on the concept of regret, and can be used for perfect information games.