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

We propose a model-free deep reinforcement learning method that leverages asmall amount of demonstration data to assist a reinforcement learning agent. Weapply this approach to robotic manipulation tasks and train end-to-endvisuomotor policies that map directly from RGB camera inputs to jointvelocities. We demonstrate that our approach can solve a wide variety ofvisuomotor tasks, for which engineering a scripted controller would belaborious. In experiments, our reinforcement and imitation agent achievessignificantly better performances than agents trained with reinforcementlearning or imitation learning alone. We also illustrate that these policies,trained with large visual and dynamics variations, can achieve preliminarysuccesses in zero-shot sim2real transfer. A brief visual description of thiswork can be viewed in https://youtu.be/EDl8SQUNjj0

Reinforcement and Imitation Learning for Diverse Visuomotor Skills