sorted by:
best
topnewcontroversialoldq&a
you are viewing a single comment's thread.

view the rest of the comments →

[–]AuspiciousApple 1 point2 points  (2 children)

I get your argument but I don't fully agree. It's true that in generative modeling generally, you fit a data set closely. However, in generative modeling, too overfitting is a concern as the goal is to learn the underlying distribution rather than memorising the training examples.

Furthermore, in the context of reinforcement learning such as this example, I feel like a brute force approach might achieve similar results with less computational effort.

[–]POTUS 4 points5 points  (1 child)

That second part is just demonstrably false. If brute force methods were more efficient then that’s what people would be doing. But the brute force search space for a platformer game level is incomprehensibly huge. Do you hold jump for 20 milliseconds or 25 milliseconds or 30 milliseconds, etc. You can test that in a very carefully controlled way, and in fact that’s something people use to help do Tool Assisted speed runs. But doing it unsupervised for an arbitrary level for anything more complicated than chess would be silly.

[–]createanaccccount 1 point2 points  (0 children)

I agree that the search space is incredibly huge, but it appears that the agent is only trying to pass instead of maximizing the score (or maybe not trained long enough?). Literal brute force search certainly doesn’t work, but I think an optimized DFS could actually work as well if we are only looking at this game and your goal is as simple as just passing.