We investigate the important problem of certifying stability of reinforcement learning policies
when interconnected with nonlinear dynamical systems. We show that by regulating the …

Reinforcement learning is a powerful paradigm for learning optimal policies from
experimental data. However, to find optimal policies, most reinforcement learning algorithms …

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 …

Machine and reinforcement learning (RL) are increasingly being applied to plan and control
the behavior of autonomous systems interacting with the physical world. Examples include …

Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …

In this work, we study model-based reinforcement learning (RL) in unknown stabilizable
linear dynamical systems. When learning a dynamical system, one needs to stabilize the …

Reinforcement learning (RL) is promising for complicated stochastic nonlinear control
problems. Without using a mathematical model, an optimal controller can be learned from …

Dealing with high variance is a significant challenge in model-free reinforcement learning
(RL). Existing methods are unreliable, exhibiting high variance in performance from run to …

Neural network controllers have become popular in control tasks thanks to their flexibility
and expressivity. Stability is a crucial property for safety-critical dynamical systems, while …

A common formulation of constrained reinforcement learning involves multiple rewards that
must individually accumulate to given thresholds. In this class of problems, we show a …