We consider reinforcement learning (RL) in episodic MDPs with adversarial full-information
reward feedback and unknown fixed transition kernels. We propose two model-free policy …

We develop a new stochastic algorithm for solving pseudomonotone stochastic variational
inequalities. Our method builds on Tseng's forward-backward-forward algorithm, which is …

Game theory is a well-established tool for studying interactions among self-interested
players. Under the assumption of complete information on the game composition at each …

We consider an online stochastic game with risk-averse agents whose goal is to learn
optimal decisions that minimize the risk of incurring significantly high costs. Specifically, we …

We formulate the novel class of contextual games, a type of repeated games driven by
contextual information at each round. By means of kernel-based regularity assumptions, we …

We consider the problem of online learning with non-convex losses. In terms of feedback,
we assume that the learner observes–or otherwise constructs–an inexact model for the loss …

The predominant paradigm in evolutionary game theory and more generally online learning
in games is based on a clear distinction between a population of dynamic agents that …

We consider monotone inclusions defined on a Hilbert space where the operator is given by
the sum of a maximal monotone operator T and a single-valued monotone, Lipschitz …

We develop a new stochastic algorithm with variance reduction for solving pseudo-
monotone stochastic variational inequalities. Our method builds on Tseng's forward …

A seminal result in game theory is von Neumann's minmax theorem, which states that zero-
sum games admit an essentially unique equilibrium solution. Classical learning results build …