Agent Societies
An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning
Similarly, Multiagent RL (MARL) can also be accelerated if agents can share knowledge with each other.
On Blame Attribution for Accountable Multi-Agent Sequential Decision Making
In this paper, we study blame attribution in the context of cooperative multi-agent sequential decision making.
On Blame Attribution for Accountable Multi-Agent Sequential Decision Making
In this paper, we study blame attribution in the context of cooperative multi-agent sequential decision making.
Multi-Agent Reinforcement Learning is A Sequence Modeling Problem
Our goal is to build the bridge between MARL and SMs so that the modeling power of modern sequence models can be unleashed for MARL.
Multi-Agent Reinforcement Learning is A Sequence Modeling Problem
Our goal is to build the bridge between MARL and SMs so that the modeling power of modern sequence models can be unleashed for MARL.
PettingZoo: A Standard API for Multi-Agent Reinforcement Learning J. K. Terry
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 with a universal, elegant Python API. PettingZoo was developed with the goal of accelerating research in Multi-Agent Reinforcement Learning ("MARL "), by making work more interchangeable, accessible and reproducible akin to what OpenAI's Gym library did for single-agent reinforcement
