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Found 18 papers, 9 papers with code
Fast computation of Nash Equilibria in Imperfect Information Games
We introduce and analyze a class of algorithms, called Mirror Ascent against an Improved Opponent (MAIO), for computing Nash equilibria in two-player zero-sum games, both in normal form and in sequential imperfect information form.
TacticAI: an AI assistant for football tactics
The utility of TacticAI is validated by a qualitative study conducted with football domain experts at Liverpool FC.
Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning
Progress in fields of machine learning and adversarial planning has benefited significantly from benchmark domains, from checkers and the classic UCI data sets to Go and Diplomacy.
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.
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).
NeuPL: Neural Population Learning
Learning in strategy games (e. g. StarCraft, poker) requires the discovery of diverse policies.
Which priors matter? Benchmarking models for learning latent dynamics
Learning dynamics is at the heart of many important applications of machine learning (ML), such as robotics and autonomous driving.
Evolutionary Dynamics and $Φ$-Regret Minimization in Games
Importantly, $\Phi$-regret enables learning agents to consider deviations from and to mixed strategies, generalizing several existing notions of regret such as external, internal, and swap regret, and thus broadening the insights gained from regret-based analysis of learning algorithms.
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.
Neural Replicator Dynamics
Policy gradient and actor-critic algorithms form the basis of many commonly used training techniques in deep reinforcement learning.
Persistent self-supervised learning principle: from stereo to monocular vision for obstacle avoidance
We study this persistent form of SSL in the context of a flying robot that has to avoid obstacles based on distance estimates from the visual cue of stereo vision.
Robotics
Designing Complex Interplanetary Trajectories for the Global Trajectory Optimization Competitions
The design of interplanetary trajectories often involves a preliminary search for options later refined/assembled into one final trajectory.
Space Physics
