765 citations · 786 across the 16 of their papers we have counts for
4 papers · 1 filter
Decentralized Mean Field Games
Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley +1
Multiagent reinforcement learning algorithms have not been widely adopted in large scale environments with many agents as they often scale poorly with the number of agents. Using m…
Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
Ken Ming Lee, Sriram Ganapathi Subramanian, Mark Crowley
Independent reinforcement learning algorithms have no theoretical guarantees for finding the best policy in multi-agent settings. However, in practice, prior works have reported go…
Partially Observable Mean Field Reinforcement Learning
Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley +1
Traditional multi-agent reinforcement learning algorithms are not scalable to environments with more than a few agents, since these algorithms are exponential in the number of agen…
Multi Type Mean Field Reinforcement Learning
Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor +1
Mean field theory provides an effective way of scaling multiagent reinforcement learning algorithms to environments with many agents that can be abstracted by a virtual mean agent.…