680 citations · 929 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
An Exchange Mechanism to Coordinate Flexibility in Residential Energy Cooperatives
Shantanu Chakraborty, Pablo Hernandez-Leal, Michael Kaisers
Energy cooperatives (ECs) such as residential and industrial microgrids have the potential to mitigate increasing fluctuations in renewable electricity generation, but only if thei…
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…
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity
Pablo Hernandez-Leal, Michael Kaisers, Tim Baarslag +1
The key challenge in multiagent learning is learning a best response to the behaviour of other agents, which may be non-stationary: if the other agents adapt their strategy as well…