Negotiating Team Formation Using Deep Reinforcement Learning

no code implementations • ICLR 2019 • Yoram Bachrach, Richard Everett, Edward Hughes, Angeliki Lazaridou, Joel Z. Leibo, Marc Lanctot, Michael Johanson, Wojciech M. Czarnecki, Thore Graepel

When autonomous agents interact in the same environment, they must often cooperate to achieve their goals.

reinforcement-learning Reinforcement Learning (RL)

Learned human-agent decision-making, communication and joint action in a virtual reality environment

no code implementations • 7 May 2019 • Patrick M. Pilarski, Andrew Butcher, Michael Johanson, Matthew M. Botvinick, Andrew Bolt, Adam S. R. Parker

In this work, we contribute a virtual reality environment wherein a human and an agent can adapt their predictions, their actions, and their communication so as to pursue a simple foraging task.

Decision Making

DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

1 code implementation • 6 Jan 2017 • Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Michael Bowling

Poker is the quintessential game of imperfect information, and a longstanding challenge problem in artificial intelligence.

Game of Poker

Computing Robust Counter-Strategies

1 code implementation • NeurIPS 2007 • Michael Johanson, Martin Zinkevich, Michael Bowling

Adaptation to other initially unknown agents often requires computing an effective counter-strategy.