3 papers
cs.LG2026
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…
cs.LG2024
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…
cs.LG2024
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…