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 …
A survey and critique of multiagent deep reinforcement learning
Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …
led to a dramatic increase in the number of applications and methods. Recent works have …
Contrasting centralized and decentralized critics in multi-agent reinforcement learning
Centralized Training for Decentralized Execution, where agents are trained offline using
centralized information but execute in a decentralized manner online, has gained popularity …
centralized information but execute in a decentralized manner online, has gained popularity …
[PDF][PDF] Is multiagent deep reinforcement learning the answer or the question? A brief survey
Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …
led to a dramatic increase in the number of applications and methods. Recent works have …
Solving multi-agent routing problems using deep attention mechanisms
Routing delivery vehicles to serve customers in dynamic and uncertain environments like
dense city centers is a challenging task that requires robustness and flexibility. Most existing …
dense city centers is a challenging task that requires robustness and flexibility. Most existing …
[HTML][HTML] A survey on multi-agent reinforcement learning and its application
Multi-agent reinforcement learning (MARL) has been a rapidly evolving field. This paper
presents a comprehensive survey of MARL and its applications. We trace the historical …
presents a comprehensive survey of MARL and its applications. We trace the historical …
Sample and communication-efficient decentralized actor-critic algorithms with finite-time analysis
Actor-critic (AC) algorithms have been widely used in decentralized multi-agent systems to
learn the optimal joint control policy. However, existing decentralized AC algorithms either …
learn the optimal joint control policy. However, existing decentralized AC algorithms either …
On centralized critics in multi-agent reinforcement learning
Abstract Centralized Training for Decentralized Execution, where agents are trained offline
in a centralized fashion and execute online in a decentralized manner, has become a …
in a centralized fashion and execute online in a decentralized manner, has become a …
A deeper understanding of state-based critics in multi-agent reinforcement learning
Abstract Centralized Training for Decentralized Execution, where training is done in a
centralized offline fashion, has become a popular solution paradigm in Multi-Agent …
centralized offline fashion, has become a popular solution paradigm in Multi-Agent …
HSVI can solve zero-sum partially observable stochastic games
State-of-the-art methods for solving 2-player zero-sum imperfect information games rely on
linear programming or regret minimization, though not on dynamic programming (DP) or …
linear programming or regret minimization, though not on dynamic programming (DP) or …