“This didn’t really work,” says Nicolas Heess, also a research scientist at DeepMind, and one of the paper’s coauthors with Lever. Because of the complexity of the problem, the huge range of options available, and the lack of prior knowledge about the task, the agents didn’t really have any idea where to start—hence the writhing and twitching.
So instead, Heess, Lever, and colleagues used neural probabilistic motor primitives (NPMP), a teaching method that nudged the AI model towards more human-like movement patterns, in the expectation that this underlying knowledge would help to solve the problem of how to move around the virtual football pitch. “It basically biases your motor control
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