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Found 15 papers, 6 papers with code
Constrained Deep Reinforcement Learning for Fronthaul Compression Optimization
In the Centralized-Radio Access Network (C-RAN) architecture, functions can be placed in the central or distributed locations.
What influences occupants' behavior in residential buildings: An experimental study on window operation in the KTH Live-In Lab
Window-opening and window-closing behaviors play an important role in indoor environmental conditions and therefore have an impact on building energy efficiency.
Conformal Off-Policy Evaluation in Markov Decision Processes
Reinforcement Learning aims at identifying and evaluating efficient control policies from data.
On the Sample Complexity of Representation Learning in Multi-task Bandits with Global and Local structure
Arms consist of two components: one that is shared across tasks (that we call representation) and one that is task-specific (that we call predictor).
Model Based Residual Policy Learning with Applications to Antenna Control
Non-differentiable controllers and rule-based policies are widely used for controlling real systems such as telecommunication networks and robots.
Analysis and Detectability of Offline Data Poisoning Attacks on Linear Dynamical Systems
In recent years, there has been a growing interest in the effects of data poisoning attacks on data-driven control methods.
Self-Tuning Tube-based Model Predictive Control
We present Self-Tuning Tube-based Model Predictive Control (STT-MPC), an adaptive robust control algorithm for uncertain linear systems with additive disturbances based on the least-squares estimator and polytopic tubes.
Tube-Based Zonotopic Data-Driven Predictive Control
We present a novel tube-based data-driven predictive control method for linear systems affected by a bounded addictive disturbance.
Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning
Importance Sampling (IS) is a widely used building block for a large variety of off-policy estimation and learning algorithms.
Balancing detectability and performance of attacks on the control channel of Markov Decision Processes
In such an attack, drawing inspiration from adversarial examples used in supervised learning, the amplitude of the adversarial perturbation is limited according to some norm, with the hope that this constraint will make the attack imperceptible.
Some Ethical Issues in the Review Process of Machine Learning Conferences
Recent successes in the Machine Learning community have led to a steep increase in the number of papers submitted to conferences.
Poisoning Attacks against Data-Driven Control Methods
This paper investigates poisoning attacks against data-driven control methods.
Data-Driven Control and Data-Poisoning attacks in Buildings: the KTH Live-In Lab case study
This work investigates the feasibility of using input-output data-driven control techniques for building control and their susceptibility to data-poisoning techniques.
Minimizing Information Leakage of Abrupt Changes in Stochastic Systems
In contrast to previous work on privacy, we study the problem for an online sequence of data.
Optimal Attacks on Reinforcement Learning Policies
Finally, we show that from the main agent perspective, the system uncertainties and the attacker can be modeled as a Partially Observable Markov Decision Process.
