5 papers
End-to-End Efficient RL for Linear Bellman Complete MDPs with Deterministic Transitions
Zakaria Mhammedi, Alexander Rakhlin, Nneka Okolo
We study reinforcement learning (RL) with linear function approximation in Markov Decision Processes (MDPs) satisfying \emph{linear Bellman completeness} -- a fundamental setting w…
Offline RL via Feature-Occupancy Gradient Ascent
Gergely Neu, Nneka Okolo
We study offline Reinforcement Learning in large infinite-horizon discounted Markov Decision Processes (MDPs) when the reward and transition models are linearly realizable under a…
Dealing with unbounded gradients in stochastic saddle-point optimization
Gergely Neu, Nneka Okolo
We study the performance of stochastic first-order methods for finding saddle points of convex-concave functions. A notorious challenge faced by such methods is that the gradients…
Offline Primal-Dual Reinforcement Learning for Linear MDPs
Germano Gabbianelli, Gergely Neu, Nneka Okolo +1
Offline Reinforcement Learning (RL) aims to learn a near-optimal policy from a fixed dataset of transitions collected by another policy. This problem has attracted a lot of attenti…
Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization
Gergely Neu, Nneka Okolo
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.…