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Found 11 papers, 5 papers with code
Guarantees for Self-Play in Multiplayer Games via Polymatrix Decomposability
Self-play is a technique for machine learning in multi-agent systems where a learning algorithm learns by interacting with copies of itself.
How to Evaluate Behavioral Models
Researchers building behavioral models, such as behavioral game theorists, use experimental data to evaluate predictive models of human behavior.
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Corrections
Hindsight rationality is an approach to playing general-sum games that prescribes no-regret learning dynamics for individual agents with respect to a set of deviations, and further describes jointly rational behavior among multiple agents with mediated equilibria.
Exploiting Action Impact Regularity and Exogenous State Variables for Offline Reinforcement Learning
Offline reinforcement learning -- learning a policy from a batch of data -- is known to be hard for general MDPs.
The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Models
Supervised learning models often make systematic errors on rare subsets of the data.
Disinformation, Stochastic Harm, and Costly Effort: A Principal-Agent Analysis of Regulating Social Media Platforms
The spread of disinformation on social platforms is harmful to society.
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games
Hindsight rationality is an approach to playing general-sum games that prescribes no-regret learning dynamics for individual agents with respect to a set of deviations, and further describes jointly rational behavior among multiple agents with mediated equilibria.
Hindsight and Sequential Rationality of Correlated Play
This approach also leads to a game-theoretic analysis, but in the correlated play that arises from joint learning dynamics rather than factored agent behavior at equilibrium.
Alternative Function Approximation Parameterizations for Solving Games: An Analysis of $f$-Regression Counterfactual Regret Minimization
In contrast, the more conventional softmax parameterization is standard in the field of reinforcement learning and yields a regret bound with a better dependence on the number of actions.
Bounds for Approximate Regret-Matching Algorithms
A common approach to incorporating function approximation is to learn the inputs needed for a regret minimizing algorithm, allowing for generalization across many regret minimization problems.
Deep Learning for Predicting Human Strategic Behavior
Predicting the behavior of human participants in strategic settings is an important problem in many domains.
