In many reinforcement learning tasks, the goal is to learn a policy to manipulate an agent,
whose design is fixed, to maximize some notion of cumulative reward. The design of the …

To realize autonomous ships in the near future, possibility of automatic berthing has been
investigated. Automatic berthing is not an easy task because of some complexities that are …

Model predictive control (MPC) is a powerful technique for solving dynamic control tasks. In
this paper, we show that there exists a close connection between MPC and online learning …

Recent advances in diffusion models have demonstrated their strong capabilities in
generating high-fidelity samples from complex distributions through an iterative refinement …

Due to high dimensionality and non-convexity, real-time optimal control using full-order
dynamics models for legged robots is challenging. Therefore, Nonlinear Model Predictive …

In this paper, we propose a topology optimization method based on the covariance matrix
adaptation evolution strategy (CMA-ES) as a method for solving multimodal structural …

We study the optimization of a continuous function by its stochastic relaxation, ie, the
optimization of the expected value of the function itself with respect to a density in a …

In this paper, we present both simultaneous and sequential algorithms for the joint
optimization of well trajectories and their life-cycle controls. The trajectory of a well is …

The covariance matrix adaptation evolution strategy (CMA-ES) is arguably one of the most
powerful real-valued derivative-free optimization algorithms, finding many applications in …

The convergence behaviors of so-called natural evolution strategies (NES) and of the
information-geometric optimization (IGO) approach are considered. After a review of the …