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John Lam's Blog


Multi-Agent Hide and Seek

This is a great visual demonstration of the progress of learning in self- supervised learning via reinforcement learning. Adding human-like behaviors to the simulation make it far more engaging to me!

The accompanying Open AI blog post does a great job of annotating the different skills learned along the way. The reward function is simple:

Agents are given a team-based reward; hiders are given a reward of +1 if all hiders are hidden and -1 if any hider is seen by a seeker. Seekers are given the opposite reward, -1 if all hiders are hidden and +1 otherwise.

From this simple reward function, many strategies emerge and many surprising "hacks" where the agents exploit loopholes in the simulation. The section on Surprising Behaviors shows these clearly.

But given the extremely large number of episodes that lead to the behaviors, I wonder how they were able to see these behaviors?

UPDATE: I just discovered the Two Minute Papers channel on YouTube and they do a great job of summarizing the paper in this video. Subscribed.