Multi-Agent Deep Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Deep Multi-Agent Reinforcement Learning with Relevance Graphs (2018, methodology): “For DQN we implemented multi-agent deep Q-learning approach which has been shown to be successful in past work [4].”
When Does Communication Learning Need Hierarchical Multi-Agent Deep Reinforcement Learning (2019, background): “developed a model that learns to negotiate and reason with other agents (Lewis et al. 2017).”
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning (2019, methodology): “TheMulti-agent Deep Q-Networks (MADQN, [3]) approachmodifies this process for multi-agent systems by performing training in two repeated steps.”
Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications (2018, background): “Egorov [19] reformulated a multi-agent environment into an image like representation and utilize convolutional neural networks to estimate Q-values for each agent in question.”
CESMA: Centralized Expert Supervises Multi-Agents (2019, background): “Others approach the non-stationarity issue by having the agents take turns updating their weights while freezing others for a time, although non-stationarity is still present (Egorov, 2016).”