254 citations · 1.1k across the 26 of their papers we have counts for
30 papers
Modeling human reputation-seeking behavior in a spatio-temporally complex public good provision game
Edward Hughes, Tina O. Zhu, Martin J. Chadwick +6
Multi-agent reinforcement learning algorithms are useful for simulating social behavior in settings that are too complex for other theoretical approaches like game theory. However,…
Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning
Jonathan Cook, Chris Lu, Edward Hughes +2
Cultural accumulation drives the open-ended and diverse progress in capabilities spanning human history. It builds an expanding body of knowledge and skills by combining individual…
Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas
Udari Madhushani, Kevin R. McKee, John P. Agapiou +6
In social psychology, Social Value Orientation (SVO) describes an individual's propensity to allocate resources between themself and others. In reinforcement learning, SVO has been…
Human-Timescale Adaptation in an Open-Ended Task Space
Adaptive Agent Team, Jakob Bauer, Kate Baumli +25
Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to rein…
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
Ian Gemp, Thomas Anthony, Yoram Bachrach +24
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 simul…
Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning
Michael Bradley Johanson, Edward Hughes, Finbarr Timbers +1
Advances in artificial intelligence often stem from the development of new environments that abstract real-world situations into a form where research can be done conveniently. Thi…
Learning Robust Real-Time Cultural Transmission without Human Data
Cultural General Intelligence Team, Avishkar Bhoopchand, Bethanie Brownfield +16
Cultural transmission is the domain-general social skill that allows agents to acquire and use information from each other in real-time with high fidelity and recall. In humans, it…
Collaborating with Humans without Human Data
DJ Strouse, Kevin R. McKee, Matt Botvinick +2
Collaborating with humans requires rapidly adapting to their individual strengths, weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement learning tech…
A multi-agent reinforcement learning model of reputation and cooperation in human groups
Kevin R. McKee, Edward Hughes, Tina O. Zhu +7
Collective action demands that individuals efficiently coordinate how much, where, and when to cooperate. Laboratory experiments have extensively explored the first part of this pr…
Modelling Cooperation in Network Games with Spatio-Temporal Complexity
Michiel A. Bakker, Richard Everett, Laura Weidinger +4
The real world is awash with multi-agent problems that require collective action by self-interested agents, from the routing of packets across a computer network to the management…
Neural Recursive Belief States in Multi-Agent Reinforcement Learning
Pol Moreno, Edward Hughes, Kevin R. McKee +2
In multi-agent reinforcement learning, the problem of learning to act is particularly difficult because the policies of co-players may be heavily conditioned on information only ob…
Open Problems in Cooperative AI
Allan Dafoe, Edward Hughes, Yoram Bachrach +5
Problems of cooperation--in which agents seek ways to jointly improve their welfare--are ubiquitous and important. They can be found at scales ranging from our daily routines--such…
Negotiating Team Formation Using Deep Reinforcement Learning
Yoram Bachrach, Richard Everett, Edward Hughes +6
When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a bin…
Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences
Raphael Köster, Kevin R. McKee, Richard Everett +7
Game theoretic views of convention generally rest on notions of common knowledge and hyper-rational models of individual behavior. However, decades of work in behavioral economics…
Learning to Incentivize Other Learning Agents
Jiachen Yang, Ang Li, Mehrdad Farajtabar +3
The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years. Much of this effort has focused on the single…
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games
Edward Hughes, Thomas W. Anthony, Tom Eccles +3
Zero-sum games have long guided artificial intelligence research, since they possess both a rich strategy space of best-responses and a clear evaluation metric. What's more, compet…
Social diversity and social preferences in mixed-motive reinforcement learning
Kevin R. McKee, Ian Gemp, Brian McWilliams +3
Recent research on reinforcement learning in pure-conflict and pure-common interest games has emphasized the importance of population heterogeneity. In contrast, studies of reinfor…
Smooth markets: A basic mechanism for organizing gradient-based learners
David Balduzzi, Wojciech M Czarnecki, Thomas W Anthony +5
With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact. Unfortunately, negative results from…
A Generalized Training Approach for Multiagent Learning
Paul Muller, Shayegan Omidshafiei, Mark Rowland +12
This paper investigates a population-based training regime based on game-theoretic principles called Policy-Spaced Response Oracles (PSRO). PSRO is general in the sense that it (1)…
OpenSpiel: A Framework for Reinforcement Learning in Games
Marc Lanctot, Edward Lockhart, Jean-Baptiste Lespiau +24
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi…
Learning Reciprocity in Complex Sequential Social Dilemmas
Tom Eccles, Edward Hughes, János Kramár +2
Reciprocity is an important feature of human social interaction and underpins our cooperative nature. What is more, simple forms of reciprocity have proved remarkably resilient in…
Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research
Joel Z. Leibo, Edward Hughes, Marc Lanctot +1
Evolution has produced a multi-scale mosaic of interacting adaptive units. Innovations arise when perturbations push parts of the system away from stable equilibria into new regime…
The Hanabi Challenge: A New Frontier for AI Research
Nolan Bard, Jakob N. Foerster, Sarath Chandar +12
From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made…
Causal Reasoning from Meta-reinforcement Learning
Ishita Dasgupta, Jane Wang, Silvia Chiappa +7
Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents. Here we explore whether causal reasoning can emerge via meta-reinf…