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Found 12 papers, 4 papers with code
An ensemble of convolution-based methods for fault detection using vibration signals
This paper focuses on solving a fault detection problem using multivariate time series of vibration signals collected from planetary gearboxes in a test rig.
Distributed Online Non-convex Optimization with Composite Regret
To address these two issues, we propose a novel composite regret with a new network regret-based metric to evaluate distributed online optimization algorithms.
Stochastic Conservative Contextual Linear Bandits
In this paper, we formulate a conservative stochastic contextual bandit formulation for real-time decision making when an adversary chooses a distribution on the set of possible contexts and the learner is subject to certain safety/performance constraints.
MDPGT: Momentum-based Decentralized Policy Gradient Tracking
We propose a novel policy gradient method for multi-agent reinforcement learning, which leverages two different variance-reduction techniques and does not require large batches over iterations.
Multi-agent Reinforcement Learning
Policy Gradient Methods
+3
A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems
We conduct further analysis on the impact of both observations and actions: on the observation end, we examine the robustness of graph-based policy on two typical data acquisition errors in power systems, namely sensor communication failure and measurement misalignment.
PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems
We introduce PowerGym, an open-source reinforcement learning environment for Volt-Var control in power distribution systems.
Differentiable Programming for Piecewise Polynomial Functions
The paradigm of differentiable programming has considerably enhanced the scope of machine learning via the judicious use of gradient-based optimization.
Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents
As the complexity of CPS evolved, the focus has shifted from traditional control methods to deep reinforcement learning-based (DRL) methods for control of these systems.
Robustifying Reinforcement Learning Agents via Action Space Adversarial Training
While robust control has a long history of development, robust ML is an emerging research area that has already demonstrated its relevance and urgency.
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
In this work, we first frame the problem as an optimization problem of minimizing the cumulative reward of an RL agent with decoupled constraints as the budget of attack.
Learning to Cope with Adversarial Attacks
Hence, it is imperative that RL agents deployed in real-life applications have the capability to detect and mitigate adversarial attacks in an online fashion.
Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning
A particularly popular form of microfluidics -- called inertial microfluidic flow sculpting -- involves placing a sequence of pillars to controllably deform an initial flow field into a desired one.
