Can I read Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space on EtoBox?

Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space by Barakat, Anas; Fatkhullin, Ilyas; He, Niao is a scholarly article available to read on EtoBox.

What is Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space about?

We consider the reinforcement learning (RL) problem with general utilities which consists in maximizing a function of the state-action occupancy measure. Beyond the standard cumulative reward RL setting, this problem includes as particular cases constrained RL, pure exploration and learning from demonstrations among others. For this problem, we propose a simpler single-loop parameter-free normalized policy gradient algorithm. Implementing a recursive momentum variance reduction mechanism, our algorithm achieves $\tilde{\mathcal{O}}(\epsilon^{-3})$ and $\tilde{\mathcal{O}}(\epsilon^{-2})$ sample complexities for $\epsilon$-first-order stationarity and $\epsilon$-global optimality respectively, under adequate assumptions. We further address the setting of large finite state action spaces via linear function approximation of the occupancy measure and show a $\tilde{\mathcal{O}}(\epsilon^{-4})$ sample complexity for a simple policy gradient method with a linear regression subroutine.

Author

Barakat, Anas; Fatkhullin, Ilyas; He, Niao

Published

2023

Language

EN