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Found 3 papers, 1 papers with code
Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement Learning
In this paper, we build on advances introduced by the Deep Q-Networks (DQN) approach to extend the multi-objective tabular Reinforcement Learning (RL) algorithm W-learning to large state spaces.
Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs
As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency.
Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space
The proposed mechanism is capable of determining the frequency with which sensors should transmit their updates, to ensure accurate collection of observations, while simultaneously considering the energy available.
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