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Found 7 papers, 2 papers with code
Interpolating Between Softmax Policy Gradient and Neural Replicator Dynamics with Capped Implicit Exploration
Neural replicator dynamics (NeuRD) is an alternative to the foundational softmax policy gradient (SPG) algorithm motivated by online learning and evolutionary game theory.
Robust No-Regret Learning in Min-Max Stackelberg Games
In this paper, we investigate the behavior of no-regret learning in min-max games with dependent strategy sets, where the strategy of the first player constrains the behavior of the second.
Convex-Concave Min-Max Stackelberg Games
Min-max optimization problems (i. e., min-max games) have been attracting a great deal of attention because of their applicability to a wide range of machine learning problems.
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.
RoxyBot-06: Stochastic Prediction and Optimization in TAC Travel
In this paper, we describe our autonomous bidding agent, RoxyBot, who emerged victorious in the travel division of the 2006 Trading Agent Competition in a photo finish.
Approximating Equilibria in Sequential Auctions with Incomplete Information and Multi-Unit Demand
In many large economic markets, goods are sold through sequential auctions.
