Chapter
Advancements in Self-Play: AI and Multiplayer Games
1:10:55 - 1:16:04 (05:09)
The speaker talks about how self-play has been able to push the professional level at the five versus five version of the game, running on a hundred thousand CPU cores, and they are still working towards the final competitive milestone for the project.
Clips
The use of self-play in multi-agent learning can lead to professional-level performance in team sports by enabling coordination and scaling up from one versus one to five versus five versions of the game.
1:10:55 - 1:12:34 (01:38)
Summary
The use of self-play in multi-agent learning can lead to professional-level performance in team sports by enabling coordination and scaling up from one versus one to five versus five versions of the game.
ChapterAdvancements in Self-Play: AI and Multiplayer Games
EpisodeGreg Brockman: OpenAI and AGI
PodcastLex Fridman Podcast
AI technology utilizes thousands of CPU cores and hundreds of GPUs to develop complex bots with unique play styles that can outsmart gamers in a variety of ways.
1:12:34 - 1:14:46 (02:12)
Summary
AI technology utilizes thousands of CPU cores and hundreds of GPUs to develop complex bots with unique play styles that can outsmart gamers in a variety of ways.
ChapterAdvancements in Self-Play: AI and Multiplayer Games
EpisodeGreg Brockman: OpenAI and AGI
PodcastLex Fridman Podcast
OpenAI views their upcoming final match against pro gamers in Dota not as an end goal, but as a final milestone in their progress of pushing the boundaries of reinforcement learning.
1:14:46 - 1:16:04 (01:18)
Summary
OpenAI views their upcoming final match against pro gamers in Dota not as an end goal, but as a final milestone in their progress of pushing the boundaries of reinforcement learning. Beyond the match, they plan to continue taking exciting next steps in their deep learning projects, with a focus on massive scale.