ReviewSensors (Basel, Switzerland)2023
Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- 6G-Enabled FANET-IoT Framework for Intelligent Watershed Monitoring Using Multi-Agent Deep Reinforcement Learning.Sensors (Basel, Switzerland) · 2026Article
- Wolf-Pack-Inspired Distributed Encirclement for UAV-USV Systems via Visual Connectivity Preservation.Biomimetics (Basel, Switzerland) · 2026Article
- Sparse Communication for Policy Shaping in Multi-Agent Reinforcement Learning.Sensors (Basel, Switzerland) · 2026Article
- Survey on Reconnaissance Autonomous Robotic Systems for Disaster Management.Sensors (Basel, Switzerland) · 2026Review
- Research on Identification of Minimum Parameter Set in Robot Dynamics and Excitation Strategy.Sensors (Basel, Switzerland) · 2025Article
- The analysis of deep reinforcement learning for dynamic graphical games under artificial intelligence.Scientific reports · 2025Article
- Swarm Intelligence for Collaborative Play in Humanoid Soccer Teams.Sensors (Basel, Switzerland) · 2025Article
- The 2025 motile active matter roadmap.Journal of physics. Condensed matter : an Institute of Physics journal · 2025Review
- Internet of Robotic Things: Current Technologies, Challenges, Applications, and Future Research Topics.Sensors (Basel, Switzerland) · 2025Review
- Making social robots adaptable and to some extent educable by a marketplace for the selection and adjustment of different interaction characters living inside a single robot.Frontiers in robotics and AI · 2025Article
- Article
- Research on multi-robot collaborative operation in logistics and warehousing using A3C optimized YOLOv5-PPO model.Frontiers in neurorobotics · 2023Article
- Benefits and Risks of Using AI Agents in Research.The Hastings Center reportArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep reinforcement learning has produced many success stories in recent years. Some example fields in which these successes have taken place include mathematics, games, health care, and robotics. In this paper, we are especially interested in multi-agent deep reinforcement learning, where multiple agents present in the environment not only learn from their own experiences but also from each other and its applications in multi-robot systems. In many real-world scenarios, one robot might not be enough to complete the given task on its own, and, therefore, we might need to deploy multiple robots who work together towards a common global objective of finishing the task. Although multi-agent deep reinforcement learning and its applications in multi-robot systems are of tremendous significance from theoretical and applied standpoints, the latest survey in this domain dates to 2004 albeit for traditional learning applications as deep reinforcement learning was not invented. We classify the reviewed papers in our survey primarily based on their multi-robot applications. Our survey also discusses a few challenges that the current research in this domain faces and provides a potential list of future applications involving multi-robot systems that can benefit from advances in multi-agent deep reinforcement learning.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.