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Data Valuation for Offline Reinforcement Learning. (arXiv:2205.09550v1 [cs.LG])
May 20, 2022, 1:12 a.m. | Amir Abolfazli, Gregory Palmer, Daniel Kudenko
cs.LG updates on arXiv.org arxiv.org
The success of deep reinforcement learning (DRL) hinges on the availability
of training data, which is typically obtained via a large number of environment
interactions. In many real-world scenarios, costs and risks are associated with
gathering these data. The field of offline reinforcement learning addresses
these issues through outsourcing the collection of data to a domain expert or a
carefully monitored program and subsequently searching for a batch-constrained
optimal policy. With the emergence of data markets, an alternative to
constructing …
arxiv data learning reinforcement reinforcement learning valuation
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