Search Results for author: Yoram Bachrach

Found 17 papers, 1 papers with code

D3C: Reducing the Price of Anarchy in Multi-Agent Learning

no code implementations1 Jan 2021 Ian Gemp, Kevin McKee, Richard Everett, Edgar Alfredo Duenez-Guzman, Yoram Bachrach, David Balduzzi, Andrea Tacchetti

Even in simple multi-agent systems, fixed incentives can lead to outcomes that are poor for the group and each individual agent.

Open Problems in Cooperative AI

no code implementations15 Dec 2020 Allan Dafoe, Edward Hughes, Yoram Bachrach, Tantum Collins, Kevin R. McKee, Joel Z. Leibo, Kate Larson, Thore Graepel

We see opportunity to more explicitly focus on the problem of cooperation, to construct unified theory and vocabulary, and to build bridges with adjacent communities working on cooperation, including in the natural, social, and behavioural sciences.

Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games

no code implementations27 Feb 2020 Edward Hughes, Thomas W. Anthony, Tom Eccles, Joel Z. Leibo, David Balduzzi, Yoram Bachrach

Here we argue that a systematic study of many-player zero-sum games is a crucial element of artificial intelligence research.

Multi-agent Reinforcement Learning

A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets

no code implementations14 Feb 2020 Gauthier Gidel, David Balduzzi, Wojciech Marian Czarnecki, Marta Garnelo, Yoram Bachrach

Adversarial training, a special case of multi-objective optimization, is an increasingly prevalent machine learning technique: some of its most notable applications include GAN-based generative modeling and self-play techniques in reinforcement learning which have been applied to complex games such as Go or Poker.

Starcraft Starcraft II

Biases for Emergent Communication in Multi-agent Reinforcement Learning

no code implementations NeurIPS 2019 Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel

We study the problem of emergent communication, in which language arises because speakers and listeners must communicate information in order to solve tasks.

Multi-agent Reinforcement Learning

A Neural Architecture for Designing Truthful and Efficient Auctions

no code implementations11 Jul 2019 Andrea Tacchetti, DJ Strouse, Marta Garnelo, Thore Graepel, Yoram Bachrach

Auctions are protocols to allocate goods to buyers who have preferences over them, and collect payments in return.

Open-ended Learning in Symmetric Zero-sum Games

no code implementations23 Jan 2019 David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech M. Czarnecki, Julien Perolat, Max Jaderberg, Thore Graepel

Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them `winner' and `loser'.

Neural Clustering By Predicting And Copying Noise

no code implementations ICLR 2018 Sam Coope, Andrej Zukov-Gregoric, Yoram Bachrach

We propose a neural clustering model that jointly learns both latent features and how they cluster.

An Attention Mechanism for Answer Selection Using a Combined Global and Local View

no code implementations5 Jul 2017 Yoram Bachrach, Andrej Zukov-Gregoric, Sam Coope, Ed Tovell, Bogdan Maksak, Jose Rodriguez, Conan McMurtie

We propose a new attention mechanism for neural based question answering, which depends on varying granularities of the input.

Answer Selection

Batch Policy Gradient Methods for Improving Neural Conversation Models

no code implementations10 Feb 2017 Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter

We study reinforcement learning of chatbots with recurrent neural network architectures when the rewards are noisy and expensive to obtain.

Chatbot Policy Gradient Methods

Cannot find the paper you are looking for? You can Submit a new open access paper.