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Dealing with unbounded gradients in stochastic saddle-point optimization
We study the performance of stochastic first-order methods for finding saddle points of convex-concave functions.
Offline Primal-Dual Reinforcement Learning for Linear MDPs
Offline Reinforcement Learning (RL) aims to learn a near-optimal policy from a fixed dataset of transitions collected by another policy.
Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization
We propose a new stochastic primal-dual optimization algorithm for planning in a large discounted Markov decision process with a generative model and linear function approximation.
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