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The world currently offers an abundance of data in multiple domains, fromwhich we can learn reinforcement learning (RL) policies without furtherinteraction with the environment. RL agents learning offline from such data ispossible but deploying them while learning might be dangerous in domains wheresafety is critical. Therefore, it is essential to find a way to estimate how anewly-learned agent will perform if deployed in the target environment beforeactually deploying it and without the risk of overestimating its trueperformance. To achieve this, we introduce a framework for safe evaluation ofoffline learning using approximate high-confidence off-policy evaluation(HCOPE) to estimate the performance of offline policies during learning. In oursetting, we assume a source of data, which we split into a train-set, to learnan offline policy, and a test-set, to estimate a lower-bound on the offlinepolicy using off-policy evaluation with bootstrapping. A lower-bound estimatetells us how good a newly-learned target policy would perform before it isdeployed in the real environment, and therefore allows us to decide when todeploy our learned policy.

Safe Evaluation For Offline Learning: Are We Ready To Deploy?