… this progress, multi-agent reinforcement learning gains rapid traction, … in the field of multi-agent
deep reinforcement learning. We … deep reinforcement learning methods with a 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-…

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

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

… 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…

… problem in multi-agent deep reinforcement learning. The surveyed … the training procedure,
such as centralized training, to learning … that learn to communicate using multi-agent deep RL. …

… deep reinforcement learning algorithms based on policy gradient, temporal-difference error,
and actor-critic methods to cooperative multi-agent … multi-agent variant of curriculum learning…

… multi-agent deep reinforcement learning (MA-DRL) has received an increased amount of
attention. Multiagent reinforcement learning … with only two implicit learning agents, lacking the …

… observability and reducing the learning difficulty of each local agent. The proposed multi-agent
A2C is compared against independent A2C and independent Q-learning algorithms, in …

… a multi-agent deep reinforcement learning scheme with each transmitter as an agent. Similar
to [30], we define the local state of learning … In the multi-agent learning system, the state …