Solving the hybrid action space problem in edge computing: a graph-aware reinforcement learning approach for container management

Heterogeneous graph attention-based deep reinforcement learning for optimal scheduling of flexible interconnected distribution substations

The spatial-temporal mismatch between generation and load is exacerbated by high distributed photovoltaic (PV) penetration in distribution service areas, causing power quality degradation and PV accommodation challenges. To tackle this issue, an end-to-end optimal scheduling method based on a heterogeneous graph attent...

Distributional Reinforcement Learning for task offloading, resource allocation and early exit selection at the edge

Simulation results confirm that integrating EE with edge computing significantly improves the trade-off between inference accuracy and latency, achieving up to 212% improvement in the average task completion ratio compared to edge computing systems without EE, under the considered simulation settings.

Diffusion-Guided Multi-Objective Reinforcement Learning for Hybrid Action Optimization in Collaborative Edge AIGC Networks

JATO: Deep Reinforcement Learning-based Joint Optimization for Task Offloading and Adaptive Transmission in Multimedia IoT Systems

JATO is presented, a framework to jointly tackle the problems of adaptive task offloading and transmission optimization using Deep Reinforcement Learning, and offers a mono-faceted solution, learning a policy to simultaneously determine the best offloading target and the transmission quality.

Deep Reinforcement Learning on Item-Compatibility Graphs for One-Dimensional Bin Packing

A novel end-to-end, size-agnostic graph reinforcement learning framework for 1D-BPP that outperforms a state-of-the-art learned solver that relies on column generation and integer programming, while using no solver at all.

Deep Reinforcement Learning for Semantic-Aware AoI Minimization in UAV-Assisted Communication Networks

Unmanned Aerial Vehicles (UAVs) are envisioned as key enablers for time-sensitive data collection in 6G cognitive networks. Semantic communication offers a promising solution to overcome bandwidth scarcity by transmitting only essential information. However, the heavy computational burden of semantic extraction is ofte...