Search Results for author: Edward Hughes

Found 22 papers, 8 papers with code

Modelling Cooperation in Network Games with Spatio-Temporal Complexity

no code implementations13 Feb 2021 Michiel A. Bakker, Richard Everett, Laura Weidinger, Iason Gabriel, William S. Isaac, Joel Z. Leibo, Edward Hughes

Such systems have local incentives for individuals, whose behavior has an impact on the global outcome for the group.

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 Incentivize Other Learning Agents

1 code implementation NeurIPS 2020 Jiachen Yang, Ang Li, Mehrdad Farajtabar, Peter Sunehag, Edward Hughes, Hongyuan Zha

The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years.

General Reinforcement Learning

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

Social diversity and social preferences in mixed-motive reinforcement learning

no code implementations6 Feb 2020 Kevin R. McKee, Ian Gemp, Brian McWilliams, Edgar A. Duéñez-Guzmán, Edward Hughes, Joel Z. Leibo

Recent research on reinforcement learning in pure-conflict and pure-common interest games has emphasized the importance of population heterogeneity.

Smooth markets: A basic mechanism for organizing gradient-based learners

no code implementations ICLR 2020 David Balduzzi, Wojciech M. Czarnecki, Thomas W. Anthony, Ian M Gemp, Edward Hughes, Joel Z. Leibo, Georgios Piliouras, Thore Graepel

With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact.

Intrinsic Social Motivation via Causal Influence in Multi-Agent RL

no code implementations ICLR 2019 Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas

Therefore, we also employ influence to train agents to use an explicit communication channel, and find that it leads to more effective communication and higher collective reward.

Multi-agent Reinforcement Learning

Learning Reciprocity in Complex Sequential Social Dilemmas

no code implementations19 Mar 2019 Tom Eccles, Edward Hughes, János Kramár, Steven Wheelwright, Joel Z. Leibo

We analyse the resulting policies to show that the reciprocating agents are strongly influenced by their co-players' behavior.

Malthusian Reinforcement Learning

no code implementations17 Dec 2018 Joel Z. Leibo, Julien Perolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel

Here we explore a new algorithmic framework for multi-agent reinforcement learning, called Malthusian reinforcement learning, which extends self-play to include fitness-linked population size dynamics that drive ongoing innovation.

Multi-agent Reinforcement Learning

Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

1 code implementation4 Nov 2018 Jakob N. Foerster, Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew Botvinick, Michael Bowling

We present the Bayesian action decoder (BAD), a new multi-agent learning method that uses an approximate Bayesian update to obtain a public belief that conditions on the actions taken by all agents in the environment.

Multi-agent Reinforcement Learning Policy Gradient Methods

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

2 code implementations ICLR 2019 Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas

We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions.

Multi-agent Reinforcement Learning

Learning to Understand Goal Specifications by Modelling Reward

1 code implementation ICLR 2019 Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Arian Hosseini, Pushmeet Kohli, Edward Grefenstette

Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards.

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