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Sim-to-Real Transfer of Robot Learning with Variable Length Inputs
Current end-to-end deep Reinforcement Learning (RL) approaches require jointly learning perception, decision-making and low-level control from very sparse reward signals and high-dimensional inputs, with little capability of incorporating prior knowledge.
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