The wobble!
Now, you may be eager to point out that the miniature bipeds aren't quite on the level of, say,, is to demonstrate the effectiveness of a form of machine learning called "deep reinforcement learning" in low-cost hardware.
As such, the researchers used an affordable and respectably capable open-source robot platform, rather than designing a robot from scratch. And clearly, their work is paying off. Not only are the artificial ankle biters agile, they also exhibit a strategic understanding of the game, the researchers said. They can mark the other player with the ball, defend their own goal, block shots, and most importantly, aim for the back of the opponent's net., the robots were trained extensively through simulations, and used simple penalties and rewards to encourage more optimal behavior.
It was through the follow-up real-world training, though, that they really began to shine. The robust bots gradually learned to be even more efficient than in the simulations, walking 156 percent faster and taking 63 percent less time to stand up, the researchers said.More on robots:
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