147 citations · 177 across the 13 of their papers we have counts for
5 papers · 1 filter
PettingZoo: Gym for Multi-Agent Reinforcement Learning
J. K. Terry, Benjamin Black, Nathaniel Grammel +10
This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle ("AEC") games model. PettingZoo is a library of diverse sets of multi-agent environments w…
Agent Environment Cycle Games
J K Terry, Nathaniel Grammel, Benjamin Black +3
Partially Observable Stochastic Games (POSGs) are the most general and common model of games used in Multi-Agent Reinforcement Learning (MARL). We argue that the POSG model is conc…
Stochastic Optimization and Learning for Two-Stage Supplier Problems
Brian Brubach, Nathaniel Grammel, David G. Harris +3
The main focus of this paper is radius-based (supplier) clustering in the two-stage stochastic setting with recourse, where the inherent stochasticity of the model comes in the for…
Multi-Agent Informational Learning Processes
J. K. Terry, Nathaniel Grammel
We introduce a new mathematical model of multi-agent reinforcement learning, the Multi-Agent Informational Learning Processor "MAILP" model. The model is based on the notion that a…
Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement Learning
J. K. Terry, Nathaniel Grammel, Sanghyun Son +2
Parameter sharing, where each agent independently learns a policy with fully shared parameters between all policies, is a popular baseline method for multi-agent deep reinforcement…