Optimizes control interventions in real-world networks using deep-learning and network science.
problem Optimizing control over socioeconomic networks subject to constraints.
method Integrates optimization tools from deep-learning with network science.
result Characterizes vulnerability of corporate networks to takeovers.
A controller learns to control a nonlinear plant with unknown model and partial observation using continuous deep Q-learning.
problem Designing a controller for a nonlinear plant with unknown model and partial sensor observation under network delays.
method Continuous deep Q-learning applied to an extended state including past control inputs and outputs.
result The controller can learn a robust control policy to network delays with partial sensor observation.
Neural network HDP improves virtual inertia control for non-inductive grids.
problem Traditional virtual inertia controllers are not suitable for non-inductive grids.
method Adaptive neural network heuristic dynamic programming (HDP) for optimal control.
result The proposed HDP controller outperforms traditional controllers in virtual inertia control.
Deep neural networks improve chemical reactor control using MPC.
problem Improving control of chemical reactors with neural networks.
method Training neural networks on model predictive control (MPC) for reactor control.
result Neural network can mimic MPC control inputs while maintaining constraints.
Langevin algorithms enhance training of deep neural networks for stochastic control problems.
problem Training acceleration for deep neural networks in stochastic control problems.
method Application of Langevin algorithms to minimize the loss of deep neural networks in stochastic control problems.
result Langevin algorithms improve training on various stochastic control problems.
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…
Designs adaptive controller for networked control systems with wireless data transmission.
problem Adaptive control in networked systems with unreliable wireless channels.
method Upper Confidence Bounds for Networked Control Systems (UCB-NCS) learning rule.
result Non-asymptotic performance guarantees with a regret bound of O(C√T).
New method approximates controllability of large networks from coarse summaries.
problem Controlling large-scale linear dynamical systems with incomplete network information.
method Algorithm using stochastic block model to estimate controllability from coarse summaries.
result Average controllability of fine-scale system can be well approximated by coarse-scale system.
Paper proposes a DRL-based controller for networked AP systems that reduces communication frequency.
problem Reduce communication frequency in networked AP systems while maintaining control performance.
method Develops a DRL-based controller that avoids explicit update timing learning, using a semi-Markov decision process (SMDP).
result Improves communication efficiency without sacrificing control performance.
Deep residual networks can approximate any continuous function using control theory.
problem Universal approximation capabilities of deep residual neural networks.
method Relating residual networks to control systems and using Lie algebraic techniques.
result Deep residual networks with adequately deep layers can approximate any continuous function on a compact set.
Paper certifies neural network control policies against persistent adversarial perturbations.
problem Neural networks' fragility to adversarial perturbations in control systems.
method Combining neural network certification tools with robust control theory.
result Certifies neural network policies in a control loop under l-infinity norm bounded adversarial perturbations.
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…
New method uses neural nets to control systems safely with disturbances.
problem Designing safe control laws for systems with disturbances.
method Imitation learning to train neural network controllers that satisfy CBF constraints.
result Demonstrated on a unicycle model with external disturbances.
MVFST-RL tackles real-time network congestion control with RL.
problem Real-time network congestion control with RL in asynchronous environments.
method Asynchronous RL framework for delayed actions in QUIC.
result Scalable framework for congestion control in QUIC with improved state-of-the-art RL.
Graph neural networks learn decentralized controllers from data.
problem Finding optimal decentralized controllers for autonomous agents is challenging.
method Adapting graph neural networks to handle delayed communications and ensure scalability and transferability.
result Graph neural networks can learn decentralized controllers from data, addressing the scalability and practical implementation issues of centralized controllers.
Since governments give stimulus to firms and expect the spillover effect by fiscal policies, it is important to know the effectiveness that they can control the economy. To clarify the controllability of the economy, we investigate a firm production network observed exhaustively in Japan and what firms should be direct…
This paper improves MARL for networked systems through new protocols and discount factors.
problem Improving control in networked systems using multi-agent reinforcement learning.
method Formulated as a spatiotemporal Markov decision process, introduced a spatial discount factor, and proposed NeurComm.
result Appropriate spatial discount factor enhances learning curves of non-communicative MARL algorithms.
Paper proposes E/PD-Control for better neural network training.
problem Training efficiency and robustness of CNNs in online data flows.
method E/PD-Control combines feedback PD controller with exponential signal.
result Better learning efficiency and robustness demonstrated experimentally.
A hybrid model combines Q-learning and PID controller for continuous vehicle control.
problem Learning unsatisfactory results with discrete action space in autonomous driving.
method Combining Q-learning and PID controller, using Quadratic Q-function approximation and action network.
result Autonomous vehicle successfully learns smooth and efficient driving behavior.
Paper proposes a new model for better engine control.
problem Optimal control problems are non-convex and hard to solve online.
method Combines Hammerstein-Wiener model with input convex neural networks.
result Optimal control problems are effectively solvable due to convexity and partial linearity.
Complexity measures for neural nets with general activations using path-based norms.
problem Control complexity of neural networks with arbitrary activation functions.
method Approximate general activations with ReLU networks and derive path-based norms for complexity control.
result Preliminary analyses of function spaces and regularized estimators.
Paper develops a neural-fuzzy controller for GPS-intelligent buoys.
problem Optimally track dynamically positioned marine buoys with unknown parameters.
method Dynamic system modeling using neural-fuzzy networks with backstepping technique.
result The controller minimizes position errors and adjusts buoy positions accurately.
Deep neural nets approximate high-dimensional HJB equations efficiently.
problem Approximating solutions to high-dimensional HJB equations.
method Deep neural networks for approximating solutions.
result Deep neural networks can approximate solutions without the curse of dimensionality.
A communication-efficient method controls FDR in network settings.
problem Controlling FDR in networks with limited communication.
method Sample-and-Forward: a flexible procedure for multihop networks.
result Nodes can control FDR without sharing p-values, achieving power and FDR control.
Optimizes wireless power control using graph neural networks and counterfactual optimization.
problem Mitigating interference in wireless networks with multiple transmitter-receiver pairs.
method Graph neural network architecture combined with unsupervised primal-dual counterfactual optimization.
result Guarantees a minimum rate constraint that adapts to network size, balancing user rates.
Two clustering algorithms optimize edge controller placement in wireless networks.
problem Optimizing edge controller placement in wireless edge networks.
method Deterministic annealing based clustering algorithms ECP-LL and ECP-LB.
result The algorithms achieve better balance between synchronization and delay costs.
The paper analyzes deep neural networks using control theory to set a time limit for their convergence.
problem Understanding the finite-time convergence of deep neural networks.
method Lyapunov based analysis of the loss function, control theory framework, finite-time control of non-linear systems.
result A priori guarantees of finite-time convergence for deep neural networks are provided.
Deep neural networks solve stochastic control problems with delay.
problem Challenges in stochastic control problems with delay due to path-dependence and high dimensions.
method Employing recurrent neural networks (RNNs) to parameterize policies and optimize objectives.
result RNNs, especially LSTMs, efficiently capture path-dependence and outperform feedforward networks in training and performance.
Paper introduces multitask neural networks for efficient stochastic control problems.
problem Infeasibility of simulating state variables in some stochastic control problems.
method Multitask neural networks with dynamic task balancing.
result Multitask neural networks outperform state-of-the-art approaches in derivatives pricing problems.
Novel method controls complex physical systems over long time frames.
problem Controlling complex nonlinear physical systems over long time frames.
method Hierarchical predictor-corrector scheme with separate planning and control networks.
result Successfully controls complex physical systems like incompressible Navier-Stokes equations.
Researchers develop a new framework to control neural network sensitivity.
problem Understanding and controlling the behavior of neural networks.
method Direct parameterization of bi-Lipschitzness in convex neural networks.
result A clear and tight control of neural network sensitivity achieved.
IG-RL learns adaptive traffic signals for any network, outperforming existing methods.
problem Adaptive traffic signal control for large networks with combinatorial state and action spaces.
method Graph-Convolutional Networks for decentralized, flexible control.
result IG-RL generalizes to new networks and traffic conditions without additional training.
The paper solves complex control problems using neural networks.
problem Solving McKean-Vlasov control problems.
method Mean-field neural networks and algorithms based on dynamic programming and stochastic maximum principle.
result Extensive numerical results show the accuracy of the proposed algorithms.
The structure of the control network of transnational corporations affects global market competition and financial stability. So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We present the first investigation of the architecture of the international …
New findings on depth vs. width in neural networks, showing depth can improve learnability.
problem Understanding the role of depth in neural networks, especially when width is unbounded.
method Analyzing sample complexity for learnability in norm-controlled depth-2 and depth-3 ReLU networks.
result Depth can improve learnability of functions that are otherwise unlearnable with depth-2 networks.
Many real world stochastic control problems suffer from the "curse of dimensionality". To overcome this difficulty, we develop a deep learning approach that directly solves high-dimensional stochastic control problems based on Monte-Carlo sampling. We approximate the time-dependent controls as feedforward neural networ…
Paper presents neural network controllers for offset-free setpoint tracking.
problem Offset-free setpoint tracking using neural network controllers.
method Exploiting slope-restricted activation functions, linear matrix inequalities are used to verify stability.
result Global and local stability conditions for neural network controllers are derived.
Deep actor-critic learning optimizes power control in mobile networks.
problem Optimizing power control in large-scale wireless mobile networks.
method Multi-agent deep reinforcement learning with deep deterministic policy gradient.
result The algorithm maximizes a global utility function in a distributed manner.
SDQL uses modular deep Q networks to efficiently learn multi-stage optimal control tasks.
problem Training complex deep reinforcement learning models for multi-stage control tasks is inefficient and unstable.
method Stacked Deep Q Learning (SDQL) with modular Q networks and backward training.
result SDQL efficiently learns optimal control policies for multi-stage tasks with high-dimensional state and action spaces.
Intelligent AQM uses ECN to predict and control network congestion.
problem Challenges in finding optimal AQM parameters due to network complexity.
method Machine Learning (Neural Network for prediction, Reinforcement Learning for parameter tuning).
result Enhanced performance of AQM using ECN and existing TCP congestion control.
Deep learning solves complex stochastic control with jumps.
problem Solving high-dimensional stochastic control tasks with jumps.
method Model-based approach using two neural networks, iteratively trained with objectives derived from the Hamilton-Jacobi-Bellman equation.
result Demonstrates effectiveness in solving complex high-dimensional stochastic control tasks.
New algorithm solves mean-field control problems using actor-critic learning with moment neural networks.
problem Solving mean-field control problems in continuous time reinforcement learning.
method Gradient-based policy and value function learning with moment neural networks on the Wasserstein space.
result Effective solution for diverse mean-field control problems, including multi-dimensional and nonlinear settings.
Review of integrating Bayesian methods with neural network-based MPC.
problem Lack of standardized benchmarks and reliable analyses in Bayesian MPC.
method Systematic analysis of Bayesian methods in neural-network-based MPC.
result Need for standardized benchmarks, ablation studies, and transparent reporting.
The paper designs neural networks with assurance for controlling nonlinear systems.
problem Designing neural networks with assurance for nonlinear system control.
method Bounding the number of affine functions needed for a CPWA function, connecting it to a TLL NN architecture.
result The TLL NN architecture is parameterized by the number of affine functions in the CPWA function it realizes.
A new network controls task execution in a single vision system.
problem Executing multiple tasks accurately and efficiently in a single network.
method A top-down control network modifies main recognition network activations based on selected task, image content, and spatial location.
result Significantly better results on four datasets compared to state-of-the-art approaches.
We use neural networks as control variates with geometric integration techniques.
problem Analytic integration of neural network approximations for variance reduction.
method Integration domain subdivision using computational geometry for MLPs with continuous piecewise linear activation functions.
result Neural networks can be used as control variates with geometric integration methods.
Adaptive traffic control uses deep RL to improve decision-making.
problem Improving traffic control using deep RL.
method Integrates recent deep RL techniques into a novel DQN-based algorithm (TC-DQN+) for traffic control.
result Proposes a new reward function for traffic control.
New control theory shows neural networks can be sparsely active over time.
problem Optimizing neural networks for long-time control with sparsity constraints.
method Proving optimal controls vanish after a positive time and providing a stability estimate.
result Optimal controls for ℓ1-penalized neural ODEs are sparsely active over time.