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 …
Multi-agent deep reinforcement learning: a survey
S Gronauer, K Diepold - Artificial Intelligence Review, 2022 - Springer
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 …
Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning
Driving safely requires multiple capabilities from human and intelligent agents, such as the
generalizability to unseen environments, the safety awareness of the surrounding traffic, and …
generalizability to unseen environments, the safety awareness of the surrounding traffic, and …
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 …
Smacv2: An improved benchmark for cooperative multi-agent reinforcement learning
The availability of challenging benchmarks has played a key role in the recent progress of
machine learning. In cooperative multi-agent reinforcement learning, the StarCraft Multi …
machine learning. In cooperative multi-agent reinforcement learning, the StarCraft Multi …
The starcraft multi-agent challenge
In the last few years, deep multi-agent reinforcement learning (RL) has become a highly
active area of research. A particularly challenging class of problems in this area is partially …
active area of research. A particularly challenging class of problems in this area is partially …
Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
Monotonic value function factorisation for deep multi-agent reinforcement learning
In many real-world settings, a team of agents must coordinate its behaviour while acting in a
decentralised fashion. At the same time, it is often possible to train the agents in a …
decentralised fashion. At the same time, it is often possible to train the agents in a …
Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles
A connected autonomous vehicle (CAV) network can be defined as a set of connected
vehicles including CAVs that operate on a specific spatial scope that may be a road network …
vehicles including CAVs that operate on a specific spatial scope that may be a road network …