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

TL;DR

MUSBO is a novel reinforcement learning framework designed for deployment constrained settings, effectively balancing exploration and exploitation by quantifying uncertainty, leading to state-of-the-art results in data-efficient policy optimization.

Contribution

The paper introduces MUSBO, a new model-based algorithm that leverages uncertainty quantification to improve data efficiency and policy updates under deployment constraints in RL.

Findings

MUSBO outperforms existing methods in deployment constrained RL tasks.

Uncertainty-aware sampling improves data efficiency and policy quality.

MUSBO achieves state-of-the-art performance in experimental benchmarks.

Abstract

In many contemporary applications such as healthcare, finance, robotics, and recommendation systems, continuous deployment of new policies for data collection and online learning is either cost ineffective or impractical. We consider a setting that lies between pure offline reinforcement learning (RL) and pure online RL called deployment constrained RL in which the number of policy deployments for data sampling is limited. To solve this challenging task, we propose a new algorithmic learning framework called Model-based Uncertainty regularized and Sample Efficient Batch Optimization (MUSBO). Our framework discovers novel and high quality samples for each deployment to enable efficient data collection. During each offline training session, we bootstrap the policy update by quantifying the amount of uncertainty within our collected data. In the high support region (low uncertainty), we encourage our policy by taking an aggressive update. In the low support region (high uncertainty) when the policy bootstraps into the out-of-distribution region, we downweight it by our estimated uncertainty quantification. Experimental results show that MUSBO achieves state-of-the-art performance in the deployment constrained RL setting.