A multi-agent low-bias deep reinforcement learning (MA-LB-DRL) scheme is proposed to solve the joint optimization problem of trajectory design, channel ...
Multi-Agent Low-Bias Reinforcement Learning for Resource Allocation in ...
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Motivated by this, we propose a multi-agent low-bias deep reinforcement learning (MA-LB-DRL) scheme to solve the joint optimization problem of trajectory design ...
Multi-agent reinforcement learning (MARL) addresses decision-making process of the multiple agents in the common environments, which is known as an ...
Oct 22, 2024 · To resolve this problem, in this paper we propose a multi-agent reinforcement learning (MARL)-based resource allocation scheme for UAV-assisted ...
Oct 24, 2018 · This article investigates dynamic resource allocation of multiple UAVs enabled communication networks with the goal of maximizing long-term rewards.
Missing: Low- Bias Assisted
Feb 1, 2024 · This paper proposes a heterogeneous multi-agent reinforcement learning (MARL)-based approach to solve the issue in high dimensions and limited states.
In this work, a weighted sum single objective optimization problem that maximizes the total network data rate while minimizing the mobility-induced handoffs and ...
Oct 24, 2018 · This article investigates dynamic resource allocation of multiple UAVs enabled communication networks with the goal of maximizing long-term.
Missing: Bias | Show results with:Bias
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Multi-agent deep Q-learning algorithms are proposed for optimizing offloading and resource management in the MEC network in [35]. Despite the enhanced ...
[PDF] Multi-Agent Reinforcement Learning-Based Resource ...
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This article investigates dynamic resource allocation of multiple UAVs enabled communication networks with the goal of maximizing long-term rewards and ...
Missing: Low- Bias