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Open Problems in Cooperative AI
Problems of cooperation–in which agents seek ways to jointly improve the...
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Game Plan: What AI can do for Football, and What Football can do for AI
The rapid progress in artificial intelligence (AI) and machine learning ...
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Negotiating Team Formation Using Deep Reinforcement Learning
When autonomous agents interact in the same environment, they must often...
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Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences
Game theoretic views of convention generally rest on notions of common k...
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EigenGame: PCA as a Nash Equilibrium
We present a novel view on principal component analysis (PCA) as a compe...
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Learning to Play No-Press Diplomacy with Best Response Policy Iteration
Recent advances in deep reinforcement learning (RL) have led to consider...
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Smooth markets: A basic mechanism for organizing gradient-based learners
With the success of modern machine learning, it is becoming increasingly...
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Biases for Emergent Communication in Multi-agent Reinforcement Learning
We study the problem of emergent communication, in which language arises...
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Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
Constructing agents with planning capabilities has long been one of the ...
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A Generalized Training Approach for Multiagent Learning
This paper investigates a population-based training regime based on game...
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A Neural Architecture for Designing Truthful and Efficient Auctions
Auctions are protocols to allocate goods to buyers who have preferences ...
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Differentiable Game Mechanics
Deep learning is built on the foundational guarantee that gradient desce...
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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...
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Emergent Coordination Through Competition
We study the emergence of cooperative behaviors in reinforcement learnin...
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Open-ended Learning in Symmetric Zero-sum Games
Zero-sum games such as chess and poker are, abstractly, functions that e...
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Malthusian Reinforcement Learning
Here we explore a new algorithmic framework for multi-agent reinforcemen...
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Relational Forward Models for Multi-Agent Learning
The behavioral dynamics of multi-agent systems have a rich and orderly s...
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Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Recent progress in artificial intelligence through reinforcement learnin...
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Adaptive Mechanism Design: Learning to Promote Cooperation
In the future, artificial learning agents are likely to become increasin...
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Re-evaluating evaluation
Progress in machine learning is measured by careful evaluation on proble...
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Inequity aversion improves cooperation in intertemporal social dilemmas
Groups of humans are often able to find ways to cooperate with one anoth...
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Inequity aversion resolves intertemporal social dilemmas
Groups of humans are often able to find ways to cooperate with one anoth...
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A Generalised Method for Empirical Game Theoretic Analysis
This paper provides theoretical bounds for empirical game theoretical an...
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The Mechanics of n-Player Differentiable Games
The cornerstone underpinning deep learning is the guarantee that gradien...
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Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
The game of chess is the most widely-studied domain in the history of ar...
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Symmetric Decomposition of Asymmetric Games
We introduce new theoretical insights into two-population asymmetric gam...
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A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
To achieve general intelligence, agents must learn how to interact with ...
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A multi-agent reinforcement learning model of common-pool resource appropriation
Humanity faces numerous problems of common-pool resource appropriation. ...
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Value-Decomposition Networks For Cooperative Multi-Agent Learning
We study the problem of cooperative multi-agent reinforcement learning w...
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Multi-agent Reinforcement Learning in Sequential Social Dilemmas
Matrix games like Prisoner's Dilemma have guided research on social dile...
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Learning Shared Representations in Multi-task Reinforcement Learning
We investigate a paradigm in multi-task reinforcement learning (MT-RL) i...
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The Wreath Process: A totally generative model of geometric shape based on nested symmetries
We consider the problem of modelling noisy but highly symmetric shapes t...
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A Comparison of learning algorithms on the Arcade Learning Environment
Reinforcement learning agents have traditionally been evaluated on small...
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Compiling Relational Database Schemata into Probabilistic Graphical Models
Instead of requiring a domain expert to specify the probabilistic depend...
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SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases
The Internet has enabled the creation of a growing number of large-scale...
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How To Grade a Test Without Knowing the Answers --- A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing
We propose a new probabilistic graphical model that jointly models the d...
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Kernel Topic Models
Latent Dirichlet Allocation models discrete data as a mixture of discret...
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