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In many contemporary applications such as healthcare, finance, robotics, andrecommendation systems, continuous deployment of new policies for datacollection and online learning is either cost ineffective or impractical. Weconsider a setting that lies between pure offline reinforcement learning (RL)and pure online RL called deployment constrained RL in which the number ofpolicy deployments for data sampling is limited. To solve this challengingtask, we propose a new algorithmic learning framework called Model-basedUncertainty regularized and Sample Efficient Batch Optimization (MUSBO). Ourframework discovers novel and high quality samples for each deployment toenable efficient data collection. During each offline training session, webootstrap the policy update by quantifying the amount of uncertainty within ourcollected data. In the high support region (low uncertainty), we encourage ourpolicy by taking an aggressive update. In the low support region (highuncertainty) when the policy bootstraps into the out-of-distribution region, wedownweight it by our estimated uncertainty quantification. Experimental resultsshow that MUSBO achieves state-of-the-art performance in the deploymentconstrained RL setting.

MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning