In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of usin...
Current computing environments are becoming increasingly complex in nature and exhibit unpredictable workloads. These environments create challenges to the design of systems that c...
Decentralized Markov decision processes are frequently used to model cooperative multi-agent systems. In this paper, we identify a subclass of general DEC-MDPs that features regul...
Coordination within decentralized agent groups frequently requires reaching global consensus, but typical hierarchical approaches to reaching such decisions can be complex, slow, ...
A significant body of work in multiagent systems over more than two decades has focused on multi-agent coordination (1). Many challenges in multi-agent coordination can be modeled ...