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COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration

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arxiv 1905.09275 v2 pith:HTTK4U5P submitted 2019-05-22 cs.LG cs.AI

COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration

Nicholas Watters , Loic Matthey , Matko Bosnjak , Christopher P. Burgess , Alexander Lerchner This is my paper
classification cs.LG cs.AI
keywords challengescobraenvironmentexplorationlearningmodel-basedobject-basedrobustness
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Data efficiency and robustness to task-irrelevant perturbations are long-standing challenges for deep reinforcement learning algorithms. Here we introduce a modular approach to addressing these challenges in a continuous control environment, without using hand-crafted or supervised information. Our Curious Object-Based seaRch Agent (COBRA) uses task-free intrinsically motivated exploration and unsupervised learning to build object-based models of its environment and action space. Subsequently, it can learn a variety of tasks through model-based search in very few steps and excel on structured hold-out tests of policy robustness.

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