Distral: Robust Multitask Reinforcement Learning

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017), pp. 4496-4506, 2017.

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Abstract:

Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data efficiency is multitask learning with shared neural network parameters, where efficiency may be improved through transfer across related tasks. In practice, how...More

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