Learning an Accurate Physics Simulator via Adversarial Reinforcement Learning

Yifeng Jiang, Research Intern and Jie Tan, Research Scientist, Robotics at Google Simulation empowers various engineering disciplines to quickly prototype with minimal human effort. In robotics, physics simulations provide a safe and inexpensive virtual playground for robots to acquire physical skills with techniques such as deep reinforcement learning (DRL). However, as the hand-derived physics in simulations does not match the real world exactly, control policies trained entirely within simula...

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