Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the
robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level …

Learning to locomote is one of the most common tasks in physics-based animation and
deep reinforcement learning (RL). A learned policy is the product of the problem to be …

Cutting planes are crucial in solving mixed integer linear programs (MILP) as they facilitate
bound improvements on the optimal solution. Modern MILP solvers rely on a variety of …

Reinforcement learning is a powerful approach to learn behaviour through interactions with
an environment. However, behaviours are usually learned in a purely reactive fashion …

We present temporally abstract actor-critic (TAAC), a simple but effective off-policy RL
algorithm that incorporates closed-loop temporal abstraction into the actor-critic framework …

In reinforcement learning, continuous time is often discretized by a time scale $\delta $, to
which the resulting performance is known to be highly sensitive. In this work, we seek to find …

Obtaining no-regret guarantees for reinforcement learning (RL) in the case of problems with
continuous state and/or action spaces is still one of the major open challenges in the field …

Reinforcement Learning (RL) has emerged as an effective approach to address a variety of
complex control tasks. In a typical RL problem, an agent interacts with the environment by …

Reinforcement learning has proven to be successful in obtaining profitable trading policies;
however, the effectiveness of such strategies is strongly conditioned to market stationarity …

Deep reinforcement learning (DRL) algorithms have been demonstrated to be effective on a
wide range of challenging decision making and control tasks. However, these methods …