Progress in fields of machine learning and adversarial planning has bene...
The Game Theory Multi-Agent team at DeepMind studies several aspects...
The designs of many large-scale systems today, from traffic routing
envi...
We introduce DeepNash, an autonomous agent capable of learning to play t...
Recent advances in multiagent learning have seen the introduction ofa fa...
The recent emergence of navigational tools has changed traffic patterns ...
The recent phenomenal success of language models has reinvigorated machi...
Mean Field Games (MFGs) can potentially scale multi-agent systems to
ext...
Concave Utility Reinforcement Learning (CURL) extends RL from linear to
...
We present a method enabling a large number of agents to learn how to fl...
We address scaling up equilibrium computation in Mean Field Games (MFGs)...
The rapid progress in artificial intelligence (AI) and machine learning ...
Regret minimization has played a key role in online learning, equilibriu...
In this paper, we deepen the analysis of continuous time Fictitious Play...
Recent advances in deep reinforcement learning (RL) have led to consider...
Multiplayer games have a long history in being used as key testbeds for
...
In this paper we investigate the Follow the Regularized Leader dynamics ...
This paper investigates a population-based training regime based on
game...
This paper investigates the evaluation of learned multiagent strategies ...
OpenSpiel is a collection of environments and algorithms for research in...
The theory of Mean Field Games (MFG) allows characterizing the Nash
equi...
This paper extends the notion of equilibrium in game theory to learning
...
In multiagent learning, agents interact in inherently nonstationary
envi...
In this paper, we present exploitability descent, a new algorithm to com...
We introduce α-Rank, a principled evolutionary dynamics methodology,
for...
Zero-sum games such as chess and poker are, abstractly, functions that
e...
Here we explore a new algorithmic framework for multi-agent reinforcemen...
Optimization of parameterized policies for reinforcement learning (RL) i...
We study the problem of learning classifiers robust to universal adversa...
Progress in machine learning is measured by careful evaluation on proble...
This paper provides theoretical bounds for empirical game theoretical
an...
We introduce new theoretical insights into two-population asymmetric gam...
To achieve general intelligence, agents must learn how to interact with
...
Humanity faces numerous problems of common-pool resource appropriation. ...