10 citations · 20 across the 6 of their papers we have counts for
6 papers
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…
Stochastic optimization with momentum: convergence, fluctuations, and traps avoidance
A. Barakat, P. Bianchi, W. Hachem +1
In this paper, a general stochastic optimization procedure is studied, unifying several variants of the stochastic gradient descent such as, among others, the stochastic heavy ball…
Convergence Analysis of a Momentum Algorithm with Adaptive Step Size for Non Convex Optimization
Anas Barakat, Pascal Bianchi
Although ADAM is a very popular algorithm for optimizing the weights of neural networks, it has been recently shown that it can diverge even in simple convex optimization examples.…
Convergence and Dynamical Behavior of the ADAM Algorithm for Non-Convex Stochastic Optimization
Anas Barakat, Pascal Bianchi
Adam is a popular variant of stochastic gradient descent for finding a local minimizer of a function. In the constant stepsize regime, assuming that the objective function is diffe…