MOReL : Model-Based Offline Reinforcement Learning

12 May 2020Rahul KidambiAravind RajeswaranPraneeth NetrapalliThorsten Joachims

In offline reinforcement learning (RL), the goal is to learn a highly rewarding policy based solely on a dataset of historical interactions with the environment. The ability to train RL policies offline can greatly expand the applicability of RL, its data efficiency, and its experimental velocity... (read more)

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