Conservative Evaluation of Offline Policy Learning
Abstract: The world offers unprecedented amounts of data in real-world domains, from which we can develop successful decision-making systems. It is possible for reinforcement learning (RL) to learn control policies offline from such data but challenging to deploy an agent during learning in safety-critical domains. Offline RL learns from historical data without access to an environment. Therefore, we need a methodology for estimating how a newly-learned agent will perform when deployed in the real environment \emph{before} actually deploying it. To achieve this, we propose a framework for conservative evaluation of offline policy learning (CEOPL). We focus on being conservative so that the probability that our agent performs below a baseline is approximately $\delta$, where $\delta$ specifies how much risk we are willing to accept. In our setting, we assume access to a data stream, split into a train-set to learn an offline policy, and a test-set to estimate a lower-bound on the offline policy using off-policy evaluation with bootstrap confidence intervals. A lower-bound estimate allows us to decide when to deploy our learned policy with minimal risk of overestimation. We demonstrate CEOPL on a range of tasks as well as real-world medical data.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Nino_Vieillard1
Submission Number: 2532
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