Enhancing Federated Reinforcement Learning: A Consensus-based Approach for Both Homogeneous and Heterogeneous Agents
Crossref DOI link: https://doi.org/10.1007/s11633-025-1550-8
Published Online: 2025-09-27
Published Print: 2025-10
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Giuseppi, Alessandro https://orcid.org/0000-0001-5503-8506
Menegatti, Danilo
Pietrabissa, Antonio
Text and Data Mining valid from 2025-09-27
Version of Record valid from 2025-09-27
Article History
Received: 30 May 2024
Accepted: 25 February 2025
First Online: 27 September 2025
Declarations of conflict of interest
: The authors declared that they have no conflicts of interest to this work.