Google Brain: Can Agents Learn Inside of their own Dreams?

worldmodels.github.io

We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment.

This is a full online paper, complete with lots of interactive elements that illustrate the points that it's making. I found the entire read fascinating; here's my favorite part:

...in our initial experiments, we noticed that our agent discovered an adversarial policy to move around in such a way so that the monsters in this virtual environment never shoots a single fireball. Even when there are signs of a fireball forming, the agent will move in a way to extinguish the fireballs magically as if it has superpowers in the environment.

Or, in simpler terms: the AI discovered cheat codes.

Read more...
Linkedin

Want to receive more content like this in your inbox?