Multi-agent learning is a crucial method to control or find solutions for systems, in which more than one entity needs to be adaptive. In today's interconnected world, such s...
Coordination within decentralized agent groups frequently requires reaching global consensus, but typical hierarchical approaches to reaching such decisions can be complex, slow, ...
Successful interaction between autonomous agents is contingent on those agents making decisions consistent with the expectations of their peers -- these expectations are based on ...
A major challenge for traditional approaches to multiagent learning is to train teams that easily scale to include additional agents. The problem is that such approaches typically...
David B. D'Ambrosio, Joel Lehman, Sebastian Risi, ...
This paper describes an algorithm, called CQ-learning, which learns to adapt the state representation for multi-agent systems in order to coordinate with other agents. We propose ...