Search Results for author:
Found 14 papers, 6 papers with code
States as Strings as Strategies: Steering Language Models with Game-Theoretic Solvers
A suitable model of the players, strategies, and payoffs associated with linguistic interactions (i. e., a binding to the conventional symbolic logic of game theory) would enable existing game-theoretic algorithms to provide strategic solutions in the space of language.
Neural Population Learning beyond Symmetric Zero-sum Games
We then introduce NeuPL-JPSRO, a neural population learning algorithm that benefits from transfer learning of skills and converges to a Coarse Correlated Equilibrium (CCE) of the game.
Evaluating Agents using Social Choice Theory
We argue that many general evaluation problems can be viewed through the lens of voting theory.
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.
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers
We argue that such a network is a powerful component for many possible multiagent algorithms.
Game Theoretic Rating in N-player general-sum games with Equilibria
Rating strategies in a game is an important area of research in game theory and artificial intelligence, and can be applied to any real-world competitive or cooperative setting.
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.
Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games
Learning to play optimally against any mixture over a diverse set of strategies is of important practical interests in competitive games.
NeuPL: Neural Population Learning
Learning in strategy games (e. g. StarCraft, poker) requires the discovery of diverse policies.
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.
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.
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).
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
Here we present results on scaling up biologically motivated models of deep learning on datasets which need deep networks with appropriate architectures to achieve good performance.
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based games.
