Agent Societies
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning
Nicolas Carion, Nicolas Usunier, Gabriel Synnaeve, Alessandro Lazaric
Neural Information Processing Systems http://nips.cc/
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Making Yizhe Huang *2,1 Xingbo Wang * 2 Hao Liu 3 Fanqi Kong 2,1
Traditional interactive environments limit agents' intelligence growth with fixed
Iteratively Learn Diverse Strategies with State Distance Information
In addition, we examine two common computation frameworks for this problem, i.e., population-based training (PBT) and iterative learning
VAST: ValueFunctionFactorizationwith VariableAgentSub-Teams
Value function factorization (VFF) is a popular approach to cooperative multiagent reinforcement learning in order to learn local value functions from global rewards.
TheSensoryNeuronasaTransformer: Permutation-InvariantNeuralNetworksfor ReinforcementLearning
In complex systems, we often observe complex global behavior emerge from a collection of agents interacting with each other in their environment, with each individual agent acting only on locally available information, without knowing thefullpicture.