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Learning Payment-Free Resource Allocation Mechanisms
We consider the design of mechanisms that allocate limited resources among self-interested agents using neural networks.
Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path
Given that the underlying Markov Decision Process (MDP) of the agent is communicating, we provide finite sample convergence guarantees in terms of convergence of the mean-field and control policy to the mean-field equilibrium.
Catoni-style Confidence Sequences under Infinite Variance
In this paper, we provide an extension of confidence sequences for settings where the variance of the data-generating distribution does not exist or is infinite.
Adversarial Linear-Quadratic Mean-Field Games over Multigraphs
In this paper, we propose a game between an exogenous adversary and a network of agents connected via a multigraph.
Extreme Bandits using Robust Statistics
We consider a multi-armed bandit problem motivated by situations where only the extreme values, as opposed to expected values in the classical bandit setting, are of interest.
Policy Gradient using Weak Derivatives for Reinforcement Learning
This paper considers policy search in continuous state-action reinforcement learning problems.
