RL agents go from face-planting to parkour when researchers keep adding network layers
While most reinforcement learning algorithms use two to five network layers, a research team achieved 2x to 50x performance gains by scaling network depth up to 1,024 layers in a self-supervised agent and saw entirely new behaviors emerge in the process.<br /> The article RL agents go from face-planting to parkour when researchers keep adding network layers appeared first on The Decoder. [...]
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