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Stochastic Halpern iteration in normed spaces and applications to reinforcement learning
We analyze the oracle complexity of the stochastic Halpern iteration with variance reduction, where we aim to approximate fixed-points of nonexpansive and contractive operators in a normed finite-dimensional space.
Bandit Learning in Concave N-Person Games
This paper examines the long-run behavior of learning with bandit feedback in non-cooperative concave games.
Bandit learning in concave $N$-person games
This paper examines the long-run behavior of learning with bandit feedback in non-cooperative concave games.
On the robustness of learning in games with stochastically perturbed payoff observations
Motivated by the scarcity of accurate payoff feedback in practical applications of game theory, we examine a class of learning dynamics where players adjust their choices based on past payoff observations that are subject to noise and random disturbances.
Reinforcement learning with restrictions on the action set
Consider a 2-player normal-form game repeated over time.
