10 citations · 20 across the 6 of their papers we have counts for
3 papers · 1 filter
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space
Anas Barakat, Ilyas Fatkhullin, Niao He
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 cumula…
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies
Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva +1
Recently, the impressive empirical success of policy gradient (PG) methods has catalyzed the development of their theoretical foundations. Despite the huge efforts directed at the…
Analysis of a Target-Based Actor-Critic Algorithm with Linear Function Approximation
Anas Barakat, Pascal Bianchi, Julien Lehmann
Actor-critic methods integrating target networks have exhibited a stupendous empirical success in deep reinforcement learning. However, a theoretical understanding of the use of ta…