arXiv research

Paper studies optimal control for a specific geometric problem.

Derives optimal control conditions using calculus of variations.

Survey of theoretical foundations for policy optimization in control.

Paper proposes a new method to optimize robot body structure and control policy.

Optimal controls for conformal Laplacian obstacle problems on spheres and manifolds.

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

Non-bilinear observations make optimal control harder, showing non-convex costs and non-affine optimal controllers.

New control methods for systems with adversarial perturbations.

We geometrically describe optimal control problems in terms of Morse families in the Hamiltonian framework. These geometric structures allow us to recover the classical first order necessary conditions for optimality and the starting point to run an integrability algorithm. Moreover the integrability algorithm is adapt…

Optimizes control interventions in real-world networks using deep-learning and network science.

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

Optimizes dividend policies in a Brownian model with controlled rates.

An energy based approach for stabilizing a mechanical system has offered a simple yet powerful control scheme. However, since it does not impose such strong constraints on parameter space of the controller, finding appropriate parameter values for an optimal controller is known to be hard. This paper intends to generat…

Learning optimal feedback control laws capable of executing optimal trajectories is essential for many robotic applications. Such policies can be learned using reinforcement learning or planned using optimal control. While reinforcement learning is sample inefficient, optimal control only plans an optimal trajectory fr…

Optimal control in latent factor models uses Tsallis entropy for exploration.

New Lagrangian approach for optimal control of second-order systems.

The paper tackles data-driven optimal control of unknown nonlinear systems using RKHS.

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

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…

Optimizes control of noisy discrete systems without system matrix knowledge.

Paper proposes a new model for better engine control.

A large body of animation research focuses on optimization of movement control, either as action sequences or policy parameters. However, as closed-form expressions of the objective functions are often not available, our understanding of the optimization problems is limited. Building on recent work on analyzing neural …

Paper uses deep reinforcement learning for better control of rocket engines during start-up phases.

Paper tackles online optimization with memory and competitive control.

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

Optimizes control of infectious disease spread using stochastic methods.

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…

We consider a new form of reinforcement learning (RL) that is based on opportunities to directly learn the optimal control policy and a general Markov decision process (MDP) framework devised to support these opportunities. Derivations of general classes of our control-based RL methods are presented, together with form…

Solves optimal control with state constraints using probabilistic methods.

The paper optimizes policies constrained to Schur stabilizing controllers using a Newton-type algorithm.

Study uses DRL with Lagrangian relaxation to solve temporal control tasks with STL constraints.

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…

Paper formulates mutual information optimal control for discrete-time systems.

A method to minimize regret in multi-agent control systems with adversarial disturbances.

Method solves learning problem with hierarchical control objectives.

A new method solves complex control problems with random coefficients.

Derives time-averaged active inference from control principles.

Nonlinear optimal control problems are often solved with numerical methods that require knowledge of system's dynamics which may be difficult to infer, and that carry a large computational cost associated with iterative calculations. We present a novel neurobiologically inspired hierarchical learning framework, Reinfor…

Optimizes exploration for nonlinear systems to learn controllers efficiently.

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…

This paper addresses the optimal control problem known as the Linear Quadratic Regulator in the case when the dynamics are unknown. We propose a multi-stage procedure, called Coarse-ID control, that estimates a model from a few experimental trials, estimates the error in that model with respect to the truth, and then d…

Study optimal control in unknown nonlinear systems with near-optimal regret bound.

Optimal control solves multi-period liability clearing problems.

Networks of coupled dynamical systems provide a powerful way to model systems with enormously complex dynamics, such as the human brain. Control of synchronization in such networked systems has far reaching applications in many domains, including engineering and medicine. In this paper, we formulate the synchronization…

Researchers develop a method to control nonlinear systems with Koopman operator regression.

New conditions prevent gaps in optimal control problems.

New framework for policy gradient methods in continuous time reinforcement learning.

We consider an optimal control problem of a property insurance company with proportional reinsurance strategy. The insurance business brings in catastrophe risk, such as earthquake and flood. The catastrophe risk could be partly reduced by reinsurance. The management of the company controls the reinsurance rate and div…