Cobra: Data-efficient model-based rl through unsupervised object discovery and curiosity-driven exploration
arXiv preprint arXiv:1905.09275, 2019•arxiv.org
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 …
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 …
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.
arxiv.org
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