Chapter
AlphaZero: self-taught game master
1:28:38 - 1:38:59 (10:20)
AlphaZero was able to reach superhuman levels of performance in games like Go, Chess, and Shogi without any rules or human input, just through trial and error. Its application is essentially limitless in any digitized domain that can be consumed by a reinforcement learning framework to sense and act in an environment.
Clips
Reinforcement learning, a type of artificial intelligence that works through reward-based learning, has shown its potential by efficiently learning to play games like Atari and Chess, but can be applied to almost any digitized domain through trial and error learning.
1:28:38 - 1:31:41 (03:02)
Summary
Reinforcement learning, a type of artificial intelligence that works through reward-based learning, has shown its potential by efficiently learning to play games like Atari and Chess, but can be applied to almost any digitized domain through trial and error learning. With the ability to learn for itself, and without any programmed rules, the possibilities for widespread application of reinforcement learning continue to increase.
ChapterAlphaZero: self-taught game master
Episode#86 – David Silver: AlphaGo, AlphaZero, and Deep Reinforcement Learning
PodcastLex Fridman Podcast
The concept of intrinsic rewards offers flexibility for discovering rewards intrinsically when the specific reward may not be specified.
1:31:41 - 1:38:59 (07:18)
Summary
The concept of intrinsic rewards offers flexibility for discovering rewards intrinsically when the specific reward may not be specified. The process of self-play in reinforcement learning can lead to the discovery of new strategies and behaviors, showcasing the essential notion of creativity in the system.