Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL

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Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL Andrew Wagenmaker 1 Kevin Huang 2 Liyiming Ke 2 Byron Boots 2 Kevin Jamieson 2 Abhishek Gupta 2 ( 1 University of California, Berkeley 2 University of Washington ajwagen@berkeley.edu, {kehuang,kayke,bboots,jamieson,abhgupta}@cs.washington.edu ) Abstract In order to mitigate the sample complexity of real-world reinforcement learning, common practice is to first train a policy in a simulator where samples are cheap

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