Setting retail electricity prices is one of the significant strategies for energy management of
multi-microgrid (MMG) systems integrated with renewable energy. Nevertheless, the need of …

Episode-based reinforcement learning (ERL) algorithms treat reinforcement learning (RL) as
a black-box optimization problem where we learn to select a parameter vector of a controller …

Current advancements in reinforcement learning (RL) have predominantly focused on
learning step-based policies that generate actions for each perceived state. While these …

Trajectory planning under kinodynamic constraints is fundamental for advanced robotics
applications that require dexterous, reactive, and rapid skills in complex environments …

Black-box optimization is a versatile approach to solve complex problems where the
objective function is not explicitly known and no higher order information is available. Due to …

We introduce a novel deep reinforcement learning (RL) approach called Movement Prmitive-
based Planning Policy (MP3). By integrating movement primitives (MPs) into the deep RL …

Reinforcement learning (RL) is a powerful approach for acquiring a good-performing policy.
However, learning diverse skills is challenging in RL due to the commonly used Gaussian …

Variational inference with Gaussian mixture models (GMMs) enables learning of highly
tractable yet multi-modal approximations of intractable target distributions with up to a few …

We present an analytical policy update rule that is independent of parametric function
approximators. The policy update rule is suitable for optimizing general stochastic policies …

Robots often rely on a repertoire of previously-learned motion policies for performing tasks
of diverse complexities. When facing unseen task conditions or when new task requirements …