Hierarchical Planning and Learning for Robots in Stochastic Settings Using Zero-Shot Option Invention
Naman Shah,
Siddharth Srivastava
Abstract:This paper addresses the problem of inventing and using hierarchical representations for stochastic robot-planning problems. Rather than using hand-coded state or action representations as input, it presents new methods for learning how to create a high-level action representation for long-horizon, sparse reward robot planning problems in stochastic settings with unknown dynamics. After training, this system yields a robot-specific but environment independent planning system. Given new problem instances in uns… Show more
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