Search Results for author:
Found 13 papers, 6 papers with code
Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning
Multiagent reinforcement learning (MARL) has benefited significantly from population-based and game-theoretic training regimes.
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
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 simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.
Learning Correlated Equilibria in Mean-Field Games
The designs of many large-scale systems today, from traffic routing environments to smart grids, rely on game-theoretic equilibrium concepts.
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
It has the additional complexity of requiring decision-making under imperfect information, similar to Texas hold'em poker, which has a significantly smaller game tree (on the order of $10^{164}$ nodes).
Learning in Mean Field Games: A Survey
Non-cooperative and cooperative games with a very large number of players have many applications but remain generally intractable when the number of players increases.
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
One limiting factor to further scale up using RL is that existing algorithms to solve MFGs require the mixing of approximated quantities such as strategies or $q$-values.
Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers
Two-player, constant-sum games are well studied in the literature, but there has been limited progress outside of this setting.
Time-series Imputation of Temporally-occluded Multiagent Trajectories
In multiagent environments, several decision-making individuals interact while adhering to the dynamics constraints imposed by the environment.
From Motor Control to Team Play in Simulated Humanoid Football
In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements such as running and turning; they then acquire mid-level football skills such as dribbling and shooting; finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds, and coordinated goal-directed behaviour as a team at the timescale of tens of seconds.
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 has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis.
Navigating the Landscape of Multiplayer Games
Multiplayer games have long been used as testbeds in artificial intelligence research, aptly referred to as the Drosophila of artificial intelligence.
A Generalized Training Approach for Multiagent Learning
This paper investigates a population-based training regime based on game-theoretic principles called Policy-Spaced Response Oracles (PSRO).
OpenSpiel: A Framework for Reinforcement Learning in Games
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
