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

The use of mobile robots is being popular over the world mainly forautonomous explorations in hazardous/ toxic or unknown environments. Thisexploration will be more effective and efficient if the explorations in unknownenvironment can be aided with the learning from past experiences. Currentlyreinforcement learning is getting more acceptances for implementing learning inrobots from the system-environment interactions. This learning can beimplemented using the concept of both single-agent and multiagent. This paperdescribes such a multiagent approach for implementing a type of reinforcementlearning using a priority based behaviour-based architecture. This proposedmethodology has been successfully tested in both indoor and outdoorenvironments.

A Behavior-based Approach for Multi-agent Q-learning for Autonomous Exploration