b'Joel Z. Leibo'

research

∙ 09/06/2023

Framework-Based Qualitative Analysis of Free Responses of Large Language Models: Algorithmic Fidelity

Today, using Large-scale generative Language Models (LLMs) it is possibl...

0 Aliya Amirova, et al. ∙

research

∙ 05/29/2023

Doing the right thing for the right reason: Evaluating artificial moral cognition by probing cost insensitivity

Is it possible to evaluate the moral cognition of complex artificial age...

0 Yiran Mao, et al. ∙

research

∙ 05/01/2023

Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas

In social psychology, Social Value Orientation (SVO) describes an indivi...

0 Udari Madhushani, et al. ∙

research

∙ 02/02/2023

Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition

Many environments contain numerous available niches of variable value, e...

0 Peter Sunehag, et al. ∙

research

∙ 11/24/2022

Melting Pot 2.0

Multi-agent artificial intelligence research promises a path to develop ...

0 John P. Agapiou, et al. ∙

research

∙ 08/10/2022

The emergence of division of labor through decentralized social sanctioning

Human ecological success relies on our characteristic ability to flexibl...

7 Anil Yaman, et al. ∙

research

∙ 05/13/2022

Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning

Advances in artificial intelligence often stem from the development of n...

0 Michael Bradley Johanson, et al. ∙

research

∙ 01/05/2022

Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria

A key challenge in the study of multiagent cooperation is the need for i...

1 Kavya Kopparapu, et al. ∙

research

∙ 10/21/2021

Statistical discrimination in learning agents

Undesired bias afflicts both human and algorithmic decision making, and ...

0 Edgar A. Duéñez-Guzmán, et al. ∙

research

∙ 07/14/2021

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot

Existing evaluation suites for multi-agent reinforcement learning (MARL)...

9 Joel Z. Leibo, et al. ∙

research

∙ 06/18/2021

Meta-control of social learning strategies

Social learning, copying other's behavior without actual experience, off...

0 Anil Yaman, et al. ∙

research

∙ 06/16/2021

A learning agent that acquires social norms from public sanctions in decentralized multi-agent settings

Society is characterized by the presence of a variety of social norms: c...

0 Eugene Vinitsky, et al. ∙

research

∙ 03/08/2021

Deep reinforcement learning models the emergent dynamics of human cooperation

Collective action demands that individuals efficiently coordinate how mu...

0 Kevin R. McKee, et al. ∙

research

∙ 02/16/2021

Quantifying environment and population diversity in multi-agent reinforcement learning

Generalization is a major challenge for multi-agent reinforcement learni...

0 Kevin R. McKee, et al. ∙

research

∙ 02/13/2021

Modelling Cooperation in Network Games with Spatio-Temporal Complexity

The real world is awash with multi-agent problems that require collectiv...

0 Michiel A. Bakker, et al. ∙

research

∙ 12/15/2020

Open Problems in Cooperative AI

Problems of cooperation–in which agents seek ways to jointly improve the...

0 Allan Dafoe, et al. ∙

research

∙ 11/13/2020

DeepMind Lab2D

We present DeepMind Lab2D, a scalable environment simulator for artifici...

0 Charles Beattie, et al. ∙

research

∙ 10/20/2020

Negotiating Team Formation Using Deep Reinforcement Learning

When autonomous agents interact in the same environment, they must often...

0 Yoram Bachrach, et al. ∙

research

∙ 10/18/2020

Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences

Game theoretic views of convention generally rest on notions of common k...

0 Raphael Koster, et al. ∙

research

∙ 02/27/2020

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

Zero-sum games have long guided artificial intelligence research, since ...

0 Edward Hughes, et al. ∙

research

∙ 02/06/2020

Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning

Recent research on reinforcement learning in pure-conflict and pure-comm...

4 Kevin R. McKee, et al. ∙

research

∙ 01/25/2020

Silly rules improve the capacity of agents to learn stable enforcement and compliance behaviors

How can societies learn to enforce and comply with social norms? Here we...

0 Raphael Koster, et al. ∙

research

∙ 01/14/2020

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

With the success of modern machine learning, it is becoming increasingly...

10 David Balduzzi, et al. ∙

research

∙ 10/29/2019

Generalization of Reinforcement Learners with Working and Episodic Memory

Memory is an important aspect of intelligence and plays a role in many d...

26 Meire Fortunato, et al. ∙

research

∙ 05/31/2019

Interval timing in deep reinforcement learning agents

The measurement of time is central to intelligent behavior. We know that...

2 Ben Deverett, et al. ∙

research

∙ 03/19/2019

Learning Reciprocity in Complex Sequential Social Dilemmas

Reciprocity is an important feature of human social interaction and unde...

0 Tom Eccles, et al. ∙

research

∙ 03/02/2019

Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research

Evolution has produced a multi-scale mosaic of interacting adaptive unit...

16 Joel Z. Leibo, et al. ∙

research

∙ 12/17/2018

Malthusian Reinforcement Learning

Here we explore a new algorithmic framework for multi-agent reinforcemen...

10 Joel Z. Leibo, et al. ∙

research

∙ 11/14/2018

Evolving intrinsic motivations for altruistic behavior

Multi-agent cooperation is an important feature of the natural world. Ma...

0 Jane X Wang, et al. ∙

research

∙ 10/19/2018

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

We propose a unified mechanism for achieving coordination and communicat...

0 Natasha Jaques, et al. ∙

research

∙ 10/19/2018

Intrinsic Social Motivation via Causal Influence in Multi-Agent RL

We derive a new intrinsic social motivation for multi-agent reinforcemen...

0 Natasha Jaques, et al. ∙

research

∙ 07/03/2018

Human-level performance in first-person multiplayer games with population-based deep reinforcement learning

Recent progress in artificial intelligence through reinforcement learnin...

2 Max Jaderberg, et al. ∙

research

∙ 04/11/2018

Emergent Communication through Negotiation

Multi-agent reinforcement learning offers a way to study how communicati...

0 Kris Cao, et al. ∙

research

∙ 03/28/2018

Unsupervised Predictive Memory in a Goal-Directed Agent

Animals execute goal-directed behaviours despite the limited range and s...

0 Greg Wayne, et al. ∙

research

∙ 03/23/2018

Inequity aversion improves cooperation in intertemporal social dilemmas

Groups of humans are often able to find ways to cooperate with one anoth...

0 Edward Hughes, et al. ∙

research

∙ 03/23/2018

Inequity aversion resolves intertemporal social dilemmas

Groups of humans are often able to find ways to cooperate with one anoth...

0 Edward Hughes, et al. ∙

research

∙ 03/16/2018

A Generalised Method for Empirical Game Theoretic Analysis

This paper provides theoretical bounds for empirical game theoretical an...

0 Karl Tuyls, et al. ∙

research

∙ 03/10/2018

Kickstarting Deep Reinforcement Learning

We present a method for using previously-trained 'teacher' agents to kic...

0 Simon Schmitt, et al. ∙

research

∙ 01/24/2018

Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents

Psychlab is a simulated psychology laboratory inside the first-person 3D...

0 Joel Z. Leibo, et al. ∙

research

∙ 07/20/2017

A multi-agent reinforcement learning model of common-pool resource appropriation

Humanity faces numerous problems of common-pool resource appropriation. ...

0 Julien Perolat, et al. ∙

research

∙ 06/16/2017

Value-Decomposition Networks For Cooperative Multi-Agent Learning

We study the problem of cooperative multi-agent reinforcement learning w...

0 Peter Sunehag, et al. ∙

research

∙ 04/12/2017

Deep Q-learning from Demonstrations

Deep reinforcement learning (RL) has achieved several high profile succe...

0 Todd Hester, et al. ∙

research

∙ 02/10/2017

Multi-agent Reinforcement Learning in Sequential Social Dilemmas

Matrix games like Prisoner's Dilemma have guided research on social dile...

0 Joel Z. Leibo, et al. ∙

research

∙ 12/12/2016

DeepMind Lab

DeepMind Lab is a first-person 3D game platform designed for research an...

0 Charles Beattie, et al. ∙

research

∙ 11/17/2016

Learning to reinforcement learn

In recent years deep reinforcement learning (RL) systems have attained s...

0 Jane X Wang, et al. ∙

research

∙ 11/16/2016

Reinforcement Learning with Unsupervised Auxiliary Tasks

Deep reinforcement learning agents have achieved state-of-the-art result...

0 Max Jaderberg, et al. ∙

research

∙ 10/20/2016

Using Fast Weights to Attend to the Recent Past

Until recently, research on artificial neural networks was largely restr...

0 Jimmy Ba, et al. ∙

research

∙ 06/14/2016

Model-Free Episodic Control

State of the art deep reinforcement learning algorithms take many millio...

0 Charles Blundell, et al. ∙

research

∙ 06/05/2016

View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation

The primate brain contains a hierarchy of visual areas, dubbed the ventr...

0 Joel Z. Leibo, et al. ∙

research

∙ 12/28/2015

Approximate Hubel-Wiesel Modules and the Data Structures of Neural Computation

This paper describes a framework for modeling the interface between perc...

0 Joel Z. Leibo, et al. ∙

Joel Z. Leibo

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