Developing a general algorithm that learns to solve tasks across a wide range of
applications has been a fundamental challenge in artificial intelligence. Although current …

We study the global linear convergence of policy gradient (PG) methods for finite-horizon
continuous-time exploratory linear-quadratic control (LQC) problems. The setting includes …

The artificial intelligence research community is continuing to grapple with the ethics of its
work by encouraging researchers to discuss potential positive and negative consequences …

We study continuous-time reinforcement learning (RL) for stochastic control in which system
dynamics are governed by jump-diffusion processes. We formulate an entropy-regularized …

Traditional Reinforcement Learning (RL) algorithms are usually applied in robotics to learn
controllers that act with a fixed control rate. Given the discrete nature of RL algorithms, they …

In reinforcement learning, temporal abstraction in the action space is a common approach to
simplifying the learning process of policies through temporally-extended courses of action …

A default assumption in reinforcement learning (RL) and optimal control is that observations
arrive at discrete time points on a fixed clock cycle. Yet, many applications involve …

In classic reinforcement learning algorithms, agents make decisions at discrete and fixed
time intervals. The duration between decisions becomes a crucial hyperparameter, as …

We study reinforcement learning (RL) for a class of continuous-time linear-quadratic (LQ)
control problems for diffusions where volatility of the state processes depends on both state …

Abstract In Reinforcement Learning, the performance of learning agents is highly sensitive to
the choice of time discretization. Agents acting at high frequencies have the best control …