TERL minimizes an estimated upper bound on action-trajectory entropy, producing more periodic and more robust locomotion policies in simulation.
Visual spatial attention and proprioceptive data-driven reinforcement learning for robust peg-in-hole task under variable conditions,
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Trajectory Entropy Reinforcement Learning for Predictable and Robust Control
TERL minimizes an estimated upper bound on action-trajectory entropy, producing more periodic and more robust locomotion policies in simulation.