Learning from Outside the Viability Kernel: Why we Should Build Robots that can Fall with Grace

18 Jun 2018Steve HeimAlexander Spröwitz

Despite impressive results using reinforcement learning to solve complex problems from scratch, in robotics this has still been largely limited to model-based learning with very informative reward functions. One of the major challenges is that the reward landscape often has large patches with no gradient, making it difficult to sample gradients effectively... (read more)

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