Search Results for author:
Found 15 papers, 7 papers with code
Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic Arm
The report describes the reinforcement learning environments created to facilitate policy learning with the UR10e, a robotic arm from Universal Robots, and presents our initial results in training deep Q-learning and proximal policy optimization agents on the developed reinforcement learning environments.
Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement Learning
The action-perception cycle in RL, however, generally assumes that a measurement of the state of the environment is available at each time step without a cost.
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
This paper provides a simulated laboratory for making use of Reinforcement Learning (RL) for chemical discovery.
Reinforcement Learning-based Wavefront Sensorless Adaptive Optics Approaches for Satellite-to-Ground Laser Communication
We propose the use of reinforcement learning (RL) to reduce the latency, size and cost of the system by up to $30-40\%$ by learning a control policy through interactions with a low-cost quadrant photodiode rather than a wavefront phase profiling camera.
Interpretable ML for Imbalanced Data
We propose a set of techniques that can be used by both deep learning model users to identify, visualize and understand class prototypes, sub-concepts and outlier instances; and by imbalanced learning algorithm developers to detect features and class exemplars that are key to model performance.
Understanding CNN Fragility When Learning With Imbalanced Data
To demystify CNN decisions on imbalanced data, we focus on their latent features.
Efficient Augmentation for Imbalanced Deep Learning
We empirically study a convolutional neural network's internal representation of imbalanced image data and measure the generalization gap between a model's feature embeddings in the training and test sets, showing that the gap is wider for minority classes.
Automated Imbalanced Classification via Layered Learning
The main contribution of our work is a new method called ICLL for tackling IBC tasks which is not based on resampling training observations.
Dynamic programming with incomplete information to overcome navigational uncertainty in a nautical environment
Using a novel toy nautical navigation environment, we show that dynamic programming can be used when only incomplete information about a partially observed Markov decision process (POMDP) is known.
Scientific Discovery and the Cost of Measurement -- Balancing Information and Cost in Reinforcement Learning
The use of reinforcement learning (RL) in scientific applications, such as materials design and automated chemistry, is increasing.
On the combined effect of class imbalance and concept complexity in deep learning
Structural concept complexity, class overlap, and data scarcity are some of the most important factors influencing the performance of classifiers under class imbalance conditions.
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification
Our $5\times2$ cross-validated results on 57 benchmark binary datasets with 9 classifiers show that RB-CCR achieves a better precision-recall trade-off than CCR and generally out-performs the state-of-the-art resampling methods in terms of AUC and G-mean.
ReMix: Calibrated Resampling for Class Imbalance in Deep learning
Class imbalance is a problem of significant importance in applied deep learning where trained models are exploited for decision support and automated decisions in critical areas such as health and medicine, transportation, and finance.
Active Measure Reinforcement Learning for Observation Cost Minimization
Our empirical evaluation demonstrates that Amrl-Q agents are able to learn a policy and state estimator in parallel during online training.
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment
Reinforcement learning (RL) has been demonstrated to have great potential in many applications of scientific discovery and design.
