Search Results for author: Thomas Anthony

Found 14 papers, 6 papers with code

Evaluating Agents using Social Choice Theory

1 code implementation5 Dec 2023 Marc Lanctot, Kate Larson, Yoram Bachrach, Luke Marris, Zun Li, Avishkar Bhoopchand, Thomas Anthony, Brian Tanner, Anna Koop

We argue that many general evaluation problems can be viewed through the lens of voting theory.

Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning

1 code implementation2 Mar 2023 Marc Lanctot, John Schultz, Neil Burch, Max Olan Smith, Daniel Hennes, Thomas Anthony, Julien Perolat

Progress in fields of machine learning and adversarial planning has benefited significantly from benchmark domains, from checkers and the classic UCI data sets to Go and Diplomacy.

Decision Making Language Modelling

Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments

no code implementations22 Sep 2022 Ian Gemp, Thomas Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome Connor, Vibhavari Dasagi, Bart De Vylder, Edgar Duenez-Guzman, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, SiQi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Perolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls

The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.

reinforcement-learning Reinforcement Learning (RL)

Multiagent Reinforcement Learning in Games with an Iterated Dominance Solution

no code implementations25 Sep 2019 Yoram Bachrach, Tor Lattimore, Marta Garnelo, Julien Perolat, David Balduzzi, Thomas Anthony, Satinder Singh, Thore Graepel

We show that MARL converges to the desired outcome if the rewards are designed so that exerting effort is the iterated dominance solution, but fails if it is merely a Nash equilibrium.

reinforcement-learning Reinforcement Learning (RL)

Neural Design of Contests and All-Pay Auctions using Multi-Agent Simulation

no code implementations25 Sep 2019 Thomas Anthony, Ian Gemp, Janos Kramar, Tom Eccles, Andrea Tacchetti, Yoram Bachrach

In contrast to auctions designed manually by economists, our method searches the possible design space using a simulation of the multi-agent learning process, and can thus handle settings where a game-theoretic equilibrium analysis is not tractable.

Thinking Fast and Slow with Deep Learning and Tree Search

4 code implementations NeurIPS 2017 Thomas Anthony, Zheng Tian, David Barber

Sequential decision making problems, such as structured prediction, robotic control, and game playing, require a combination of planning policies and generalisation of those plans.

Decision Making reinforcement-learning +2

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