This paper studies the robustness of reinforcement learning algorithms to errors in the
learning process. Specifically, we revisit the benchmark problem of discrete-time linear …

We study stochastic policy gradient methods from the perspective of control-theoretic
limitations. Our main result is that ill-conditioned linear systems in the sense of Doyle …

Model-free learning-based control methods have seen great success recently. However,
such methods typically suffer from poor sample complexity and limited convergence …

Many reinforcement learning methods achieve great success in practice but lack theoretical
foundation. In this paper, we study the convergence analysis of the model-free methods for …

The scalability of multi-agent reinforcement learning methods to a large number of
population is drawing more and more attention in both practice and theory. We consider the …

The interface between machine learning and control has enabled robots to move outside the
laboratory into challenging real-world settings. Deep reinforcement learning can scale …

Reinforcement learning (RL) has been widely studied by researchers and utilized in different
real-life applications by practitioners recently, due to its recent successful applications to the …