Optimization of the Functional Decomposition of Parallel and Distributed Computations in Graph Coloring With the Use of High-Performance Computing

Škrinárová, Jarmila; Dudáš, Adam

doi:10.1109/access.2022.3162215

Cited by 6 publications

(1 citation statement)

References 22 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Table 2, the abbreviation LS indicates the number of neurons allocated for the latent space, which was determined experimentally to be half the number of features in the state space. In the future, the training process is expected to be accelerated through distributed parallelization [45][46][47]. The cumulative intrinsic and extrinsic rewards obtained by an agent throughout a single episode are represented by the intrinsic and extrinsic scores shown in the following figures.…”

Section: Resultsmentioning

confidence: 99%

Signal Novelty Detection as an Intrinsic Reward for Robotics

Kubovčík

Ľuptáková

Pospı́chal

2023

Sensors

View full text Add to dashboard Cite

In advanced robot control, reinforcement learning is a common technique used to transform sensor data into signals for actuators, based on feedback from the robot’s environment. However, the feedback or reward is typically sparse, as it is provided mainly after the task’s completion or failure, leading to slow convergence. Additional intrinsic rewards based on the state visitation frequency can provide more feedback. In this study, an Autoencoder deep learning neural network was utilized as novelty detection for intrinsic rewards to guide the search process through a state space. The neural network processed signals from various types of sensors simultaneously. It was tested on simulated robotic agents in a benchmark set of classic control OpenAI Gym test environments (including Mountain Car, Acrobot, CartPole, and LunarLander), achieving more efficient and accurate robot control in three of the four tasks (with only slight degradation in the Lunar Lander task) when purely intrinsic rewards were used compared to standard extrinsic rewards. By incorporating autoencoder-based intrinsic rewards, robots could potentially become more dependable in autonomous operations like space or underwater exploration or during natural disaster response. This is because the system could better adapt to changing environments or unexpected situations.

show abstract

Section: Resultsmentioning

confidence: 99%

Signal Novelty Detection as an Intrinsic Reward for Robotics

Kubovčík

Ľuptáková

Pospı́chal

2023

Sensors

View full text Add to dashboard Cite

show abstract

Performance of DNS Over HTTPS Implementation on Low-Power Devices

Huraj,

Hrinkino,

Simon

2023

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Predictive analysis visualization component in simulated data streams

Dudáš,

Demian

2024

Discov Computing

View full text Add to dashboard Cite

One of the most significant problems related to Big Data is their analysis with the use of various methods from the area of descriptive statistics or machine and deep learning. This process is interesting in both—static datasets containing various data sources which do not change over time, and dynamic datasets collected with the use of ambient data sources, which measure a number of attribute values over long periods. Since access to actual dynamic data systems is demanding, the focus of this work is put on the design and implementation of a framework usable in a simulation of data streams, their processing and subsequent dynamic predictive and visual analysis. The proposed system is experimentally verified in the context of a case study conducted on an environmental variable dataset, which was measured with the use of a real-life sensor network.

show abstract

Optimization of the Functional Decomposition of Parallel and Distributed Computations in Graph Coloring With the Use of High-Performance Computing

Cited by 6 publications

References 22 publications

Signal Novelty Detection as an Intrinsic Reward for Robotics

Signal Novelty Detection as an Intrinsic Reward for Robotics

Performance of DNS Over HTTPS Implementation on Low-Power Devices

Predictive analysis visualization component in simulated data streams

Contact Info

Product

Resources

About