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Found 8 papers, 2 papers with code
Enhancing Reinforcement Learning Agents with Local Guides
This paper addresses the problem of integrating local guide policies into a Reinforcement Learning agent.
Improving a Proportional Integral Controller with Reinforcement Learning on a Throttle Valve Benchmark
This paper presents a learning-based control strategy for non-linear throttle valves with an asymmetric hysteresis, leading to a near-optimal controller without requiring any prior knowledge about the environment.
A Conservative Approach for Few-Shot Transfer in Off-Dynamics Reinforcement Learning
In the few-shot framework, a limited number of transitions from the target environment are introduced to facilitate a more effective transfer.
Sparse Dynamical Features generation, application to Parkinson's Disease diagnosis
In this study we focus on the diagnosis of Parkinson's Disease (PD) based on electroencephalogram (EEG) signals.
Enhancing Robustness of On-line Learning Models on Highly Noisy Data
Classification algorithms have been widely adopted to detect anomalies for various systems, e. g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i. e., features and labels are correctly set.
Event-Based Control for Online Training of Neural Networks
During its training the learning rate and the gradient are two key factors to tune for influencing the convergence speed of the model.
Feedback Control for Online Training of Neural Networks
Convolutional neural networks (CNNs) are commonly used for image classification tasks, raising the challenge of their application on data flows.
RAD: On-line Anomaly Detection for Highly Unreliable Data
Classification algorithms have been widely adopted to detect anomalies for various systems, e. g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i. e., features and labels are correctly set.
