by Ciro Santilli (@cirosantilli, 35)
Video 1.
Creating Multimodal Interactive Agents from DeepMind by Two Minute Papers (2023)
Source. www.deepmind.com/blog/building-interactive-agents-in-video-game-worlds
Video 2.
Open-Ended Learning Leads to Generally Capable Agents by DeepMind (2021)
Short name: XLand. Whitepaper: www.deepmind.com/blog/generally-capable-agents-emerge-from-open-ended-play.
TODO get one of the games running. Instructions: github.com/deepmind/lab/blob/master/docs/users/build.md. This may helpgithub.com/deepmind/lab/issues/242: "Complete installation script for Ubuntu 20.04".
It is interesting how much overlap some of those have with Ciro's 2D reinforcement learning games
The games are 3D, but most of them are purely flat, and the 3D is just a waste of resources.
Video 1.
Human player test of DMLab-30 Collect Good Objects task by DeepMind (2018)
Source.
Video 2.
Human player test of DMLab-30 Exploit Deferred Effects task by DeepMind (2018)
Source.
Video 3.
Human player test of DMLab-30 Select Described Object task by DeepMind (2018)
Source. Some of their games involve language instructions from the use to determine the desired task, cool concept.
Video 4.
Human player test of DMLab-30 Fixed Large Map task by DeepMind (2018)
Source. They also have some maps with more natural environments.
Very similar to gvgai, Julian Togelius actually called them out on that: DeepMind Lab2D vs gvgai.
TODO get running, publish demo videos on YouTube.
Figure 1. Source.
At twitter.com/togelius/status/1328404390114435072 called out on DeepMind Lab2D for not giving them credit on prior work!
This very much looks like like GVGAI which was first released in 2014, been used in dozens (maybe hundreds) of papers, and for which one of the original developers was Tom Schaul at DeepMind...
As seen from web.archive.org/web/20220331022932/http://gvgai.net/ though, DeepMind sponsored them at some point.

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