English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Reinforcement learning (RL) is a promising approach for optimizing HVACcontrol. RL offers a framework for improving system performance, reducingenergy consumption, and enhancing cost efficiency. We benchmark two popularclassical and deep RL methods (Q-Learning and Deep-Q-Networks) across multipleHVAC environments and explore the practical consideration of modelhyper-parameter selection and reward tuning. The findings provide insight forconfiguring RL agents in HVAC systems, promoting energy-efficient andcost-effective operation.

A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control