arXiv research

Paper proposes tensor-based method for semiconductor manufacturing process control.

MFRL-BI controls manufacturing processes without needing accurate models.

Study optimal control of diffusion processes with infimum or supremum costs.

Anticipatory model generates music with control over events.

We approach the development of models and control strategies of susceptible-infected-susceptible (SIS) epidemic processes from the perspective of marked temporal point processes and stochastic optimal control of stochastic differential equations (SDEs) with jumps. In contrast to previous work, this novel perspective is…

The paper solves a control problem using reflections to track a benchmark process.

Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process. Real world systems would realistically fail or break before an optimal controller can be learned. To address this issue, we propose a cont…

Unified approach for data-driven control of stochastic processes.

Optimizes dividend control in a bankruptcy process using a special Levy process.

For controlled discrete-time stochastic processes we introduce a new class of dynamic risk measures, which we call process-based. Their main features are that they measure risk of processes that are functions of the history of a base process. We introduce a new concept of conditional stochastic time consistency and we …

Paper uses CNN to predict process parameters from molten pool data in WLAM.

Deep RL controller outperforms market making benchmarks in a Hawkes process model.

Solves optimal control for stochastic processes with absorbing states.

Combines Gaussian processes and polynomial chaos for stochastic control.

New approach to control diffusion processes with soft constraints.

New method tackles model uncertainty in stochastic control using Bayesian nonparametrics.

Optimizes dividend policies in a Brownian model with controlled rates.

Two methods monitor high-dimensional processes via manifold fitting or learning.

For large-scale industrial processes under closed-loop control, process dynamics directly resulting from control action are typical characteristics and may show different behaviors between real faults and normal changes of operating conditions. However, conventional distributed monitoring approaches do not consider the…

Unified reinforcement learning and stochastic processes with action-driven processes.

We solve a complex trade execution problem by simplifying it into a known LQ control problem.

We study stochastic differential equations (SDEs) whose drift and diffusion coefficients are path-dependent and controlled. We construct a value process on the canonical path space, considered simultaneously under a family of singular measures, rather than the usual family of processes indexed by the controls. This val…

We consider a zero-sum stochastic differential controller-and-stopper game in which the state process is a controlled diffusion evolving in a multi-dimensional Euclidean space. In this game, the controller affects both the drift and the volatility terms of the state process. Under appropriate conditions, we show that t…

This work proposes a new method for simultaneous probabilistic identification and control of an observable, fully-actuated mechanical system. Identification is achieved by conditioning stochastic process priors on observations of configurations and noisy estimates of configuration derivatives. In contrast to previous w…

Control Barrier Functions (CBF) have been recently utilized in the design of provably safe feedback control laws for nonlinear systems. These feedback control methods typically compute the next control input by solving an online Quadratic Program (QP). Solving QP in real-time can be a computationally expensive process …

In this paper we consider an energy storage optimization problem in finite time in a model with partial information that allows for a changing economic environment. The state process consists of the storage level controlled by the storage manager and the energy price process, which is a diffusion process the drift of w…

Modern automation systems rely on closed loop control, wherein a controller interacts with a controlled process, based on observations. These systems are increasingly complex, yet most controllers are linear Proportional-Integral-Derivative (PID) controllers. PID controllers perform well on linear and near-linear syste…

A framework integrates machine learning with robust control for safer, more reliable systems.

A model optimizes carbon emission reduction and allowance purchasing for companies.

Risk-controlled post-processing optimizes decision policies under risk constraints.

Optimizes costs in uncertain Markov systems using risk filters.

We solve a broad class of sequential decision-making problems with partially observed states.

Study shows how to control jump-diffusion processes with stable feedback controls in reinforcement learning.

This paper develops numerical methods for finding optimal dividend pay-out and reinsurance policies. A generalized singular control formulation of surplus and discounted payoff function are introduced, where the surplus is modeled by a regime-switching process subject to both regular and singular controls. To approxima…

Study uses viscosity solutions to solve control problems involving measure-valued martingales.

We consider a controlled diffusion process $(X_t)_{t\ge 0}$ where the controller is allowed to choose the drift $μ_t$ and the volatility $σ_t$ from a set $\K(x) \subset \R\times (0,\infty)$ when $X_t=x$. By choosing the largest $\fracμ{σ^2}$ at every point in time an extremal process is constructed which is under suita…

Nonparametric adaptive robust control tackles model uncertainty in stochastic processes.

Abstract: Surveying connections between ML and Control Theory.

The paper tackles learning to control systems with unknown parameters using Brownian noise.

Optimal investment and risk control strategies for insurers are derived using a time-consistent approach.

A new control chart detects shifts in binary data streams quickly and reliably.

One of the fundamental assumptions in stochastic control of continuous time processes is that the dynamics of the underlying (diffusion) process is known. This is, however, usually obviously not fulfilled in practice. On the other hand, over the last decades, a rich theory for nonparametric estimation of the drift (and…

This paper tackles inventory control with general arrival dynamics and post-processing, improving profitability.

A reliable controller is critical and essential for the execution of safe and smooth maneuvers of an autonomous vehicle.The controller must be robust to external disturbances, such as road surface, weather, and wind conditions, and so on.It also needs to deal with the internal parametric variations of vehicle sub-syste…

This article is concerned with learning and stochastic control in physical systems which contain unknown input signals. These unknown signals are modeled as Gaussian processes (GP) with certain parametrized covariance structures. The resulting latent force models (LFMs) can be seen as hybrid models that contain a first…

We are interested in understanding stability (almost sure boundedness) of stochastic approximation algorithms (SAs) driven by a `controlled Markov' process. Analyzing this class of algorithms is important, since many reinforcement learning (RL) algorithms can be cast as SAs driven by a `controlled Markov' process. In t…

The paper extends Merton's problem by adding benchmark tracking, finding optimal strategies.

Study uses multi-agent reinforcement learning to control self-assembly with high-resolution external control.