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

In recent years, deep reinforcement learning has achieved significant resultsin low-level controlling tasks. However, the problem of control smoothness hasless attention. In autonomous driving, unstable control is inevitable since thevehicle might suddenly change its actions. This problem will lower thecontrolling system's efficiency, induces excessive mechanical wear, and causesuncontrollable, dangerous behavior to the vehicle. In this paper, we apply theConditioning for Action Policy Smoothness (CAPS) with image-based input tosmooth the control of an autonomous miniature car racing. Applying CAPS andsim-to-real transfer methods helps to stabilize the control at a higher speed.Especially, the agent with CAPS and CycleGAN reduces 21.80% of the averagefinishing lap time. Moreover, we also conduct extensive experiments to analyzethe impact of CAPS components.

Image-Based Conditioning for Action Policy Smoothness in Autonomous Miniature Car Racing with Reinforcement Learning