[PDF][PDF] Deep reinforcement learning for dynamic multichannel access
We consider the problem of dynamic multichannel access in a Wireless Sensor Network
(WSN) containing N correlated channels, where the states of these channels follow a joint …
(WSN) containing N correlated channels, where the states of these channels follow a joint …
Deep reinforcement learning radio control and signal detection with kerlym, a gym rl agent
This paper presents research in progress investigating the viability and adaptation of
reinforcement learning using deep neural network based function approximation for the task …
reinforcement learning using deep neural network based function approximation for the task …
QFlow: A reinforcement learning approach to high QoE video streaming over wireless networks
Wireless Internet access has brought legions of heterogeneous applications all sharing the
same resources. However, current wireless edge networks that cater to worst or average …
same resources. However, current wireless edge networks that cater to worst or average …
Toward joint learning of optimal MAC signaling and wireless channel access
A Valcarce, J Hoydis - IEEE Transactions on Cognitive …, 2021 - ieeexplore.ieee.org
Communication protocols are the languages used by network nodes. Before a user
equipment (UE) exchanges data with a base station (BS), it must first negotiate the …
equipment (UE) exchanges data with a base station (BS), it must first negotiate the …
Exploration with unreliable intrinsic reward in multi-agent reinforcement learning
This paper investigates the use of intrinsic reward to guide exploration in multi-agent
reinforcement learning. We discuss the challenges in applying intrinsic reward to multiple …
reinforcement learning. We discuss the challenges in applying intrinsic reward to multiple …
[HTML][HTML] Exploring deep reinforcement learning with multi q-learning
E Duryea, M Ganger, W Hu - Intelligent Control and Automation, 2016 - scirp.org
Q-learning is a popular temporal-difference reinforcement learning algorithm which often
explicitly stores state values using lookup tables. This implementation has been proven to …
explicitly stores state values using lookup tables. This implementation has been proven to …
Distributionally Robust -Learning
Reinforcement learning (RL) has demonstrated remarkable achievements in simulated
environments. However, carrying this success to real environments requires the important …
environments. However, carrying this success to real environments requires the important …
Distributed deep reinforcement learning-based spectrum and power allocation for heterogeneous networks
This paper investigates the problem of distributed resource management in two-tier
heterogeneous networks, where each cell selects its joint device association, spectrum …
heterogeneous networks, where each cell selects its joint device association, spectrum …
A finite-time analysis of Q-learning with neural network function approximation
Q-learning with neural network function approximation (neural Q-learning for short) is
among the most prevalent deep reinforcement learning algorithms. Despite its empirical …
among the most prevalent deep reinforcement learning algorithms. Despite its empirical …