Multi-agent reinforcement learning: A selective overview of theories and algorithms
Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …
has registered tremendous success in solving various sequential decision-making problems …
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 …
Towards continual reinforcement learning: A review and perspectives
In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
Fully decentralized multi-agent reinforcement learning with networked agents
We consider the fully decentralized multi-agent reinforcement learning (MARL) problem,
where the agents are connected via a time-varying and possibly sparse communication …
where the agents are connected via a time-varying and possibly sparse communication …
Communication-efficient and distributed learning over wireless networks: Principles and applications
Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …
Learning multiagent communication with backpropagation
S Sukhbaatar, R Fergus - Advances in neural information …, 2016 - proceedings.neurips.cc
Many tasks in AI require the collaboration of multiple agents. Typically, the communication
protocol between agents is manually specified and not altered during training. In this paper …
protocol between agents is manually specified and not altered during training. In this paper …
Independent policy gradient for large-scale markov potential games: Sharper rates, function approximation, and game-agnostic convergence
We examine global non-asymptotic convergence properties of policy gradient methods for
multi-agent reinforcement learning (RL) problems in Markov potential games (MPGs). To …
multi-agent reinforcement learning (RL) problems in Markov potential games (MPGs). To …
Global convergence of multi-agent policy gradient in markov potential games
Potential games are arguably one of the most important and widely studied classes of
normal form games. They define the archetypal setting of multi-agent coordination as all …
normal form games. They define the archetypal setting of multi-agent coordination as all …
Reinforcement learning approach for optimal distributed energy management in a microgrid
In this paper, a multiagent-based model is used to study distributed energy management in
a microgrid (MG). The suppliers and consumers of electricity are modeled as autonomous …
a microgrid (MG). The suppliers and consumers of electricity are modeled as autonomous …
Multi-UAV trajectory planning for energy-efficient content coverage: A decentralized learning-based approach
In next-generation wireless networks, high-mobility unmanned aerial vehicles (UAVs) are
promising to provide content coverage, where users can receive sufficient requested content …
promising to provide content coverage, where users can receive sufficient requested content …