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

Most Reinforcement Learning (RL) methods are traditionally studied in anactive learning setting, where agents directly interact with theirenvironments, observe action outcomes, and learn through trial and error.However, allowing partially trained agents to interact with real physicalsystems poses significant challenges, including high costs, safety risks, andthe need for constant supervision. Offline RL addresses these cost and safetyconcerns by leveraging existing datasets and reducing the need forresource-intensive real-time interactions. Nevertheless, a substantialchallenge lies in the demand for these datasets to be meticulously annotatedwith rewards. In this paper, we introduce Optimal Transport Reward (OTR)labelling, an innovative algorithm designed to assign rewards to offlinetrajectories, using a small number of high-quality expert demonstrations. Thecore principle of OTR involves employing Optimal Transport (OT) to calculate anoptimal alignment between an unlabeled trajectory from the dataset and anexpert demonstration. This alignment yields a similarity measure that iseffectively interpreted as a reward signal. An offline RL algorithm can thenutilize these reward signals to learn a policy. This approach circumvents theneed for handcrafted rewards, unlocking the potential to harness vast datasetsfor policy learning. Leveraging the SurRoL simulation platform tailored forsurgical robot learning, we generate datasets and employ them to train policiesusing the OTR algorithm. By demonstrating the efficacy of OTR in a differentdomain, we emphasize its versatility and its potential to expedite RLdeployment across a wide range of fields.

Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments