Multi-agent reinforcement learning: A review of challenges and applications
In this review, we present an analysis of the most used multi-agent reinforcement learning
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …
An overview of multi-agent reinforcement learning from game theoretical perspective
Y Yang, J Wang - arxiv preprint arxiv:2011.00583, 2020 - arxiv.org
Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
[PDF][PDF] Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents
Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum of tasks …
significant improvements, enabling them to generalize across a broad spectrum of tasks …
Multi-agent deep reinforcement learning: a survey
The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …
Although the multi-agent domain has been overshadowed by its single-agent counterpart …
Exploring collaboration mechanisms for llm agents: A social psychology view
As Natural Language Processing (NLP) systems are increasingly employed in intricate
social environments, a pressing query emerges: Can these NLP systems mirror human …
social environments, a pressing query emerges: Can these NLP systems mirror human …
Machine learning for reliability engineering and safety applications: Review of current status and future opportunities
Abstract Machine learning (ML) pervades an increasing number of academic disciplines and
industries. Its impact is profound, and several fields have been fundamentally altered by it …
industries. Its impact is profound, and several fields have been fundamentally altered by it …
Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning
We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in
the centralized training with decentralized execution (CTDE) regime popularized recently …
the centralized training with decentralized execution (CTDE) regime popularized recently …
Survey of deep reinforcement learning for motion planning of autonomous vehicles
S Aradi - IEEE Transactions on Intelligent Transportation …, 2020 - ieeexplore.ieee.org
Academic research in the field of autonomous vehicles has reached high popularity in
recent years related to several topics as sensor technologies, V2X communications, safety …
recent years related to several topics as sensor technologies, V2X communications, safety …
Reinforcement learning in robotic applications: a comprehensive survey
In recent trends, artificial intelligence (AI) is used for the creation of complex automated
control systems. Still, researchers are trying to make a completely autonomous system that …
control systems. Still, researchers are trying to make a completely autonomous system that …
Digital transformation in the resource and energy sectors: A systematic review
The forces of digital transformation have delivered significant benefits like sustainable
development and economic growth in a range of early adopter industries such as retail and …
development and economic growth in a range of early adopter industries such as retail and …