… deep reinforcement learning beyond single-agent scenarios, with more consideration of
multi-agent … and systematic overview of multi-agent deep reinforcement learning methods in …

… of multi-agent deep reinforcement learning. We focus primarily on literature from recent
years that combines deep reinforcement learning methods with a multi-agent … in the multi-agent …

… multi-agent systems using deep reinforcement learning (DRL). In particular, we explore if
effective multi-agent … In deep reinforcement learning, a neural network is used to estimate the Q-…

… We call the stabilized IA2C the multi-agent A2C (MA2C). MA2C is evaluated in both a
synthetic large traffic grid and a real-world large traffic network, with delicately designed traffic …

… Deep Reinforcement Learning has made significant progress in multi-agent systems in …
an overview of recent approaches on Multi-Agent Reinforcement Learning (MARL) algorithms. …

… of single-agent deep reinforcement learning algorithms … multi-agent systems. To effectively
scale these algorithms beyond a trivial number of agents, we combine them with a multi-agent …

… one agent which motivates the importance of multi-agent learning techniques. Furthermore,
… role of DRL with an emphasis on deep Multi-Agent Reinforcement Learning (MARL) for AI-…

… on two multi-agent environment suites: a discrete version of the multiple-particle environment
(MPE) (Lowe et al., 2017; Wang et al., 2020) and the Starcraft multi-agent challenge (SMAC…

… In such multi-agent environments, additional learning problems … -stationarity problem in
multi-agent deep reinforcement learning. … that learn to communicate using multi-agent deep RL. …

… field of multi-agent deep reinforcement learning (MA-DRL) has received an increased amount
of attention. Multiagent reinforcement … faced within multiagent reinforcement learning is the …