In the empirical approach to game-theoretic analysis (EGTA), the model of the game comes
not from declarative representation, but is derived by interrogation of a procedural …

Designing an incentive compatible auction that maximizes expected revenue is an intricate
task. The single-item case was resolved in a seminal piece of work by Myerson in 1981, but …

Game theory provides a mathematical way to study the interaction between multiple
decision makers. However, classical game-theoretic analysis is limited in scalability due to …

A significant robustness gap exists between machine intelligence and human perception
despite recent advances in deep learning. Deep learning is not provably secure. A critical …

A recent body of experimental literature has studied empirical game-theoretical analysis, in
which we have partial knowledge of a game, consisting of observations of a subset of the …

Deep reinforcement learning (RL) is a powerful method for generating policies in complex
environments, and recent breakthroughs in game-playing have leveraged deep RL as part …

Algorithmic pricing on online e-commerce platforms raises the concern of tacit collusion,
where reinforcement learning algorithms learn to set collusive prices in a decentralized …

Policy space response oracles (PSRO) is a multi-agent reinforcement learning algorithm that
has achieved state-of-the-art performance in very large two-player zero-sum games. PSRO …

Overcomplete latent representations have been very popular for unsupervised feature
learning in recent years. In this paper, we specify which overcomplete models can be …

Policy-Space Response Oracles (PSRO) as a general algorithmic framework has achieved
state-of-the-art performance in learning equilibrium policies of two-player zero-sum games …