A survey on scheduling techniques in computing and network convergence
S Tang, Y Yu, H Wang, G Wang, W Chen… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …
computing power. This trend results in the urgent need for higher-level computing resource …
DHAA: Distributed heuristic action aware multi-agent path finding in high density scene
D Zhou, Z Pang, W Li - Multimedia Tools and Applications, 2024 - Springer
Multi-agent path finding (MAPF) in highly structured environments is an exciting and
complex problem. Compared with lower-density environments, the problems of agent credit …
complex problem. Compared with lower-density environments, the problems of agent credit …
Designing, Developing, and Validating Network Intelligence for Scaling in Service-Based Architectures based on Deep Reinforcement Learning
Automating network processes without human intervention is crucial for the complex 6G
environment. This requires zero-touch management and orchestration, the integration of …
environment. This requires zero-touch management and orchestration, the integration of …
To the Max: Reinventing Reward in Reinforcement Learning
In reinforcement learning (RL), different rewards can define the same optimal policy but
result in drastically different learning performance. For some, the agent gets stuck with a …
result in drastically different learning performance. For some, the agent gets stuck with a …
To the Max
G Veviurko, JW Böhmer, MM de Weerdt - 2024 - repository.tudelft.nl
In reinforcement learning (RL), different reward functions can define the same optimal policy
but result in drastically different learning performance. For some, the agent gets stuck with a …
but result in drastically different learning performance. For some, the agent gets stuck with a …