arXiv research

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

Meta-reinforcement learning improves fault-adaptive control efficiency.

TSAC achieves optimal frequentist regret in adaptive control of LQRs.

New algorithm reduces control error in systems with changing dynamics.

Survey combines FL and control for better adaptability and privacy.

In this paper we propose a new methodology for solving an uncertain stochastic Markovian control problem in discrete time. We call the proposed methodology the adaptive robust control. We demonstrate that the uncertain control problem under consideration can be solved in terms of associated adaptive robust Bellman equa…

Adaptive traffic control uses deep RL to improve decision-making.

This paper tackles adaptive control of unknown Markov jump systems with sample complexity and regret bounds.

AdaptOn achieves logarithmic regret in adaptive control of unknown partially observable linear systems.

Paper proposes adaptive control for unknown systems using reinforcement learning.

Agents acting in the natural world aim at selecting appropriate actions based on noisy and partial sensory observations. Many behaviors leading to decision mak- ing and action selection in a closed loop setting are naturally phrased within a control theoretic framework. Within the framework of optimal Control Theory, o…

In this paper a neural network heuristic dynamic programing (HDP) is used for optimal control of the virtual inertia based control of grid connected three phase inverters. It is shown that the conventional virtual inertia controllers are not suited for non inductive grids. A neural network based controller is proposed …

Nonparametric adaptive robust control tackles model uncertainty in stochastic processes.

LqgOpt learns optimal control in unknown LQG systems with minimal regret.

Combines control variates and adaptive importance sampling for Monte Carlo integration.

Designs adaptive controller for networked control systems with wireless data transmission.

Despite decades of research and recent progress in adaptive control and reinforcement learning, there remains a fundamental lack of understanding in designing controllers that provide robustness to inherent non-asymptotic uncertainties arising from models estimated with finite, noisy data. We propose a robust adaptive …

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

We consider adaptive control of the Linear Quadratic Regulator (LQR), where an unknown linear system is controlled subject to quadratic costs. Leveraging recent developments in the estimation of linear systems and in robust controller synthesis, we present the first provably polynomial time algorithm that provides high…

New bounds for adaptive control in high dimensions without fixed state space.

Latent FxLMS accelerates ANC by adapting along low-dimensional filter weights.

Novel Bayesian meta-reinforcement learning framework improves traffic signal control robustness.

This paper improves MARL for networked systems through new protocols and discount factors.

CAD-DA controls anomaly detection under domain adaptation.

New model-based methods adapt pre-trained policies to unseen environments efficiently.

NCT simplifies one-step generator adaptation to new controls.

We investigate the adaptive robust control framework for portfolio optimization and loss-based hedging under drift and volatility uncertainty. Adaptive robust problems offer many advantages but require handling a double optimization problem (infimum over market measures, supremum over the control) at each instance. Mor…

The study sets limits on how well systems can be controlled adaptively.

FinFlowRL combines imitation and reinforcement learning for better financial control.

Paper proposes a privacy-preserving method to control false discoveries.

Adaptive optimal control using value iteration initiated from a stabilizing control policy is theoretically analyzed in terms of stability of the system during the learning stage without ignoring the effects of approximation errors. This analysis includes the system operated using any single/constant resulting control …

Convolutional neural networks (CNNs) are commonly used for image classification tasks, raising the challenge of their application on data flows. During their training, adaptation is often performed by tuning the learning rate. Usual learning rate strategies are time-based i.e. monotonously decreasing. In this paper, we…

Adapts model-based advice to stabilize black-box policies for nonlinear control.

Syntax designs adaptive trials for subpopulations with potential benefits.

IG-RL learns adaptive traffic signals for any network, outperforming existing methods.

The study optimizes wind farm yaw control using Gaussian process regression and high-fidelity simulations.

Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived "worst" case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and r…

Paper tackles non-Markovian control problems with new learning methods.

In a voice-controlled smart-home, a controller must respond not only to user's requests but also according to the interaction context. This paper describes Arcades, a system which uses deep reinforcement learning to extract context from a graphical representation of home automation system and to update continuously its…

Optimal control in changing systems without strong convexity assumptions.

This paper improves Adam's performance in machine learning tasks.

Truncated backpropagation through time (TBPTT) is a popular method for learning in recurrent neural networks (RNNs) that saves computation and memory at the cost of bias by truncating backpropagation after a fixed number of lags. In practice, choosing the optimal truncation length is difficult: TBPTT will not converge …

Paper proposes a deep RL approach for traffic signal control balancing efficiency and equity.

A new framework for adaptive behavior using reusable value profiles.

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

ACS is an interactive framework for model-free selection with guaranteed error control.

Paper develops a neural-fuzzy controller for GPS-intelligent buoys.

A nonlinear wave alternative for the standard Black-Scholes option-pricing model is presented. The adaptive-wave model, representing 'controlled Brownian behavior' of financial markets, is formally defined by adaptive nonlinear Schrödinger (NLS) equations, defining the option-pricing wave function in terms of the stock…