Latest pitch for AI: DeepMind-trained soccer robots
The researchers had to be careful to stop the goal-scoring training when agents fell on the ground to prevent undesirable but evidently functional behavior:"Without this termination, agents find a local minimum and learn to roll on the ground towards the ball to knock it into the goal, rather than walking and kicking," they explain in their paper.
Shifting the trained software agent into a robot body proved none too difficult. It was a zero-shot process, according to the authors, meaning they didn't have to do additional training. That is to say, they made sure the simulator parameters mapped to hardware actuator settings, randomized characteristics like floor friction and joint orientation, the mass of robot parts, control loop latency, and random perturbations, all to ensure the software could handle a variety of forces acting upon the robot's body. In one adjustment, they added a reward component that encouraged the bots to put less stress on their knee joints, which otherwise had a tendency to get damaged.
"The reinforcement learning policy performed better than the specialized manually-designed skills: it walked 156 percent faster and took 63 percent less time to get up," the paper says.
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