45 citations · 145 across the 15 of their papers we have counts for
4 papers · 1 filter
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…
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning
Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
In this paper we explore how actor-critic methods in deep reinforcement learning, in particular Asynchronous Advantage Actor-Critic (A3C), can be extended with agent modeling. Insp…
Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition
Chao Gao, Pablo Hernandez-Leal, Bilal Kartal +1
The Pommerman Team Environment is a recently proposed benchmark which involves a multi-agent domain with challenges such as partial observability, decentralized execution (without…
A Survey and Critique of Multiagent Deep Reinforcement Learning
Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have…