arXiv research

Derives optimal control conditions using calculus of variations.

Study proves optimal controls for stochastic Volterra equations with singular kernels.

Optimizes control of infectious disease spread using stochastic methods.

A new method for stochastic optimal control improves accuracy over existing techniques.

Study optimizes trading in multiple assets with cross-effects.

Paper solves a control problem with robust methods.

A new ML algorithm solves complex economic control problems.

The paper solves a complex control problem with stochastic elements and switching conditions.

Optimizes dividend policies in a Brownian model with controlled rates.

The Noether theorem is extended to stochastic control problems using contact symmetries.

A number of optimal decision problems with uncertainty can be formulated into a stochastic optimal control framework. The Least-Squares Monte Carlo (LSMC) algorithm is a popular numerical method to approach solutions of such stochastic control problems as analytical solutions are not tractable in general. This paper ge…

Solves optimal control for stochastic processes with absorbing states.

FinFlowRL learns from experts to optimize financial control in changing markets.

This paper develops scalable control variates for Monte Carlo methods using stochastic optimization.

The paper solves optimal control problems for stochastic delay equations.

Study optimizes resource allocation in noisy systems for better control.

Optimizes control of noisy discrete systems without system matrix knowledge.

Paper develops a novel approach for optimal control using kernel methods.

NOVAS uses adaptive stochastic search for non-convex optimization in deep networks.

New algorithm achieves optimal regret in non-stochastic control, showing stochasticity is not beneficial.

Paper introduces multitask neural networks for efficient stochastic control problems.

Optimizes trading in markets with unpredictable price impacts.

Deep neural networks solve stochastic control problems with delay.

Optimal control of stochastic nonlinear dynamical systems is a major challenge in the domain of robot learning. Given the intractability of the global control problem, state-of-the-art algorithms focus on approximate sequential optimization techniques, that heavily rely on heuristics for regularization in order to achi…

We propose a novel reformulation of the stochastic optimal control problem as an approximate inference problem, demonstrating, that such a interpretation leads to new practical methods for the original problem. In particular we characterise a novel class of iterative solutions to the stochastic optimal control problem …

There are over 15 distinct communities that work in the general area of sequential decisions and information, often referred to as decisions under uncertainty or stochastic optimization. We focus on two of the most important fields: stochastic optimal control, with its roots in deterministic optimal control, and reinfo…

A new method solves complex control problems with random coefficients.

In this note, we study a class of stochastic control problems where the optimal strategies are described by two parameters. These include a subset of singular control, impulse control, and two-player stochastic games. The parameters are first chosen by the two continuous/smooth fit conditions, and then the optimality o…

New control theory for self-path-dependent problems solves unique constraints.

Optimizes CM for stochastic convex optimization with progressive precision.

The paper tackles robust control with uncertain dependence using data-driven methods.

New method uses backward SDEs for deep learning uncertainty.

This work formalizes guidance in diffusion models and introduces a stochastic control framework.

A new approach optimizes weights in DLP for better risk-adjusted performance.

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

Efficient algorithm for unknown linear systems with convex costs.

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

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

We present a novel technique to solve the problem of managing optimally a pumped hydroelectric storage system. This technique relies on representing the system as a stochastic optimal control problem with state constraints, these latter corresponding to the finite volume of the reservoirs. Following the recent level-se…

This paper first describes a class of uncertain stochastic control systems with Markovian switching, and derives an Itô-Liu formula for Markov-modulated processes. And we characterize an optimal control law, which satisfies the generalized Hamilton-Jacobi-Bellman (HJB) equation with Markovian switching. Then, by using …

New metric derived for robust optimization in stochastic control problems.

Combines Gaussian processes and polynomial chaos for stochastic control.

New method solves nonseparable stochastic control problems.

Optimal withdrawal strategy for DC pension plans maximizes total withdrawals while managing risk.

This paper investigates methods for estimating the optimal stochastic control policy for a Markov Decision Process with unknown transition dynamics and an unknown reward function. This form of model-free reinforcement learning comprises many real world systems such as playing video games, simulated control tasks, and r…

We consider a stochastic control problem with the assumption that the system is controlled until the state process breaks the fixed barrier. Assuming some general conditions, it is proved that the resulting Hamilton Jacobi Bellman equations has smooth solution. The aforementioned result is used to solve the optimal div…

This paper studies a class of continuous-time scalar-state stochastic Linear-Quadratic (LQ) optimal control problem with the linear control constraints. Applying the state separation theorem induced from its special structure, we develop the explicit solution for this class of problem. The revealed optimal control poli…

Nonparametric adaptive robust control tackles model uncertainty in stochastic processes.