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

02/23/2021
by   DiJia Su, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

06/05/2020

Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization

Most reinforcement learning (RL) algorithms assume online access to the ...
06/26/2020

Critic Regularized Regression

Offline reinforcement learning (RL), also known as batch RL, offers the ...
05/27/2020

MOPO: Model-based Offline Policy Optimization

Offline reinforcement learning (RL) refers to the problem of learning po...
02/26/2022

Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons

We consider reinforcement learning (RL) methods in offline domains witho...
12/16/2020

Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation

Most of the existing deep reinforcement learning (RL) approaches for ses...
11/29/2021

Robust On-Policy Data Collection for Data-Efficient Policy Evaluation

This paper considers how to complement offline reinforcement learning (R...
07/21/2020

EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL

Off-policy reinforcement learning (RL) holds the promise of sample-effic...