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

Decision Transformers (DT) have demonstrated strong performances in offlinereinforcement learning settings, but quickly adapting to unseen novel tasksremains challenging. To address this challenge, we propose a new framework,called Hyper-Decision Transformer (HDT), that can generalize to novel tasksfrom a handful of demonstrations in a data- and parameter-efficient manner. Toachieve such a goal, we propose to augment the base DT with an adaptationmodule, whose parameters are initialized by a hyper-network. When encounteringunseen tasks, the hyper-network takes a handful of demonstrations as inputs andinitializes the adaptation module accordingly. This initialization enables HDTto efficiently adapt to novel tasks by only fine-tuning the adaptation module.We validate HDT's generalization capability on object manipulation tasks. Wefind that with a single expert demonstration and fine-tuning only 0.5% of DTparameters, HDT adapts faster to unseen tasks than fine-tuning the whole DTmodel. Finally, we explore a more challenging setting where expert actions arenot available, and we show that HDT outperforms state-of-the-art baselines interms of task success rates by a large margin.

Hyper-Decision Transformer for Efficient Online Policy Adaptation