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Jul 27, 2021

DeepMind: Generally capable agents emerge from open-ended play

Posted by in categories: entertainment, robotics/AI

I’ve been suggesting for a long time to drop these Ai’s into open world games.


EDIT: Also see paper and results compilation video!

Today, we published “Open-Ended Learning Leads to Generally Capable Agents,” a preprint detailing our first steps to train an agent capable of playing many different games without needing human interaction data. … The result is an agent with the ability to succeed at a wide spectrum of tasks — from simple object-finding problems to complex games like hide and seek and capture the flag, which were not encountered during training. We find the agent exhibits general, heuristic behaviours such as experimentation, behaviours that are widely applicable to many tasks rather than specialised to an individual task.

The neural network architecture we use provides an attention mechanism over the agent’s internal recurrent state — helping guide the agent’s attention with estimates of subgoals unique to the game the agent is playing.

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