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Curiosity & Entropy Driven Unsupervised RL in Multiple Environments
We primarily propose and experiment with five new modifications to the original work: sampling trajectories using an entropy-based probability distribution, dynamic alpha, higher KL Divergence threshold, curiosity-driven exploration, and alpha-percentile sampling on curiosity.
Learning Generalizable Tool-use Skills through Trajectory Generation
We propose to learn a generative model of the tool-use trajectories as a sequence of tool point clouds, which generalizes to different tool shapes.
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