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Found 11 papers, 3 papers with code
Deep Reinforcement Learning for Autonomous Cyber Operations: A Survey
An overview of state-of-the-art approaches for scaling DRL to domains that confront learners with the curse of dimensionality, and; iv.)
Cutting Through the Noise: An Empirical Comparison of Psychoacoustic and Envelope-based Features for Machinery Fault Detection
We train a state-of-the-art one-class-classifier, on samples from healthy motors and separate the faulty ones for fault detection using a threshold.
Developing an AI-enabled IIoT platform -- Lessons learned from early use case validation
For a broader adoption of AI in industrial production, adequate infrastructure capabilities are crucial.
Market Making with Scaled Beta Policies
This paper introduces a new representation for the actions of a market maker in an order-driven market.
Data Valuation for Offline Reinforcement Learning
The success of deep reinforcement learning (DRL) hinges on the availability of training data, which is typically obtained via a large number of environment interactions.
Identifying Cause-and-Effect Relationships of Manufacturing Errors using Sequence-to-Sequence Learning
In car-body production the pre-formed sheet metal parts of the body are assembled on fully-automated production lines.
Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition
The proposed framework consists of i) federated learning for data privacy, and ii) adversarial training at the training stage and randomisation at the testing stage for model robustness.
A deep learning approach to identify unhealthy advertisements in street view images
Our project presents a novel implementation for the incidental classification of street view images for identifying unhealthy advertisements, providing a means through which to identify areas that can benefit from tougher advertisement restriction policies for tackling social inequalities.
The Automated Inspection of Opaque Liquid Vaccines
We train 3D-ConvNets to predict the likelihood of 20-frame video samples containing anomalies.
Negative Update Intervals in Deep Multi-Agent Reinforcement Learning
For instance, hysteretic Q-learning addresses miscoordination while leaving agents vulnerable towards misleading stochastic rewards.
Lenient Multi-Agent Deep Reinforcement Learning
We find that LDQN agents are more likely to converge to the optimal policy in a stochastic reward CMOTP compared to standard and scheduled-HDQN agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
