Towards Off-policy Evaluation as a Prerequisite for Real-world Reinforcement Learning in Building Control

2Citations
Citations of this article
25Readers
Mendeley users who have this article in their library.

Abstract

We present an initial study of off-policy evaluation (OPE), a problem prerequisite to real-world reinforcement learning (RL), in the context of building control. OPE is the problem of estimating a policy's performance without running it on the actual system, using historical data from the existing controller. It enables the control engineers to ensure a new, pretrained policy satisfies the performance requirements and safety constraints of a real-world system, prior to interacting with it. While many methods have been developed for OPE, no study has evaluated which ones are suitable for building operational data, which are generated by deterministic policies and have limited coverage of the state-action space. After reviewing existing works and their assumptions, we adopted the approximate model (AM) method. Furthermore, we used bootstrapping to quantify uncertainty and correct for bias. In a simulation study, we evaluated the proposed approach on 10 policies pretrained with imitation learning. On average, the AM method estimated the energy and comfort costs with 1.84% and 14.1% error, respectively.

Cite

CITATION STYLE

APA

Chen, B., Jin, M., Wang, Z., Hong, T., & Bergés, M. (2020). Towards Off-policy Evaluation as a Prerequisite for Real-world Reinforcement Learning in Building Control. In RLEM 2020 - Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings and Cities (pp. 52–56). Association for Computing Machinery, Inc. https://doi.org/10.1145/3427773.3427871

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free