gregdeon

Incredible work! I recently gave a talk about TASing to an audience of CS grad students, and of course I had to mention your SMB3 runs. Your videos are phenomenal at making this stuff accessible outside of hardcore gaming circles.

The entire TAS file takes about 16 MB, far more than the 4 KB of RAM on the NES. During the audio + video playback, the TAS is streaming via the controller by making inputs roughly 500 times per frame (15 kHz).

I'm not sure about Gurobi or how the author used it in this case. But in general, yes: these combinatorial solvers construct proof trees showing that, no matter how you assign the variables, you can't find a better solution. In simpler cases you can literally inspect the proof tree and check how it's reached a contradiction. I imagine the proof tree from this article would be an obscenely large object, but in principle you could inspect it here too.

What a pioneer. RIP Byuu.

Wow, incredibly interesting to see what one of these manuals looks like! Thanks for sharing.

Yes: the reason why some data can be compressed is because many of its bits are predictable, meaning that it has low entropy per bit.

I think it is. Latency was just one of the problems he described. A physical controller sometimes adds "phantom inputs" as the joystick transitions between two inputs. Physical actuators also slow down with wear. A physical Atari-playing robot needs to learn qualitatively different strategies that are somewhat more robust to these problems. Emulators also let the bot take as much time as it needs between frames, which is much easier than playing in real time. To me, all of this makes a physical robot seem like a decent way to start engaging with problems that come up in robotics but not simulated games.

I watched the talk live. I felt that his main argument was that Atari _looks_ solved, but there's still plenty of value that could be gained by revisiting these "solved" games. For one, learning how to play games through a physical interface is a way to start engaging with the kinds of problems that make robotics hard (e.g., latency). They're also a good environment to study catastrophic forgetting: an hour of training on one game shouldn't erase a model's ability to play other games.

I think we could eventually saturate Atari, but for now it looks like it's still a good source of problems that are just out of reach of current methods.

Wonderfully written article! Thanks for sharing.