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Multi-Agent Reinforcement Learning-Based Resource Allocation for UAV Networks
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DOI：10.1109/TWC.2019.2935201.png)
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摘要
En 中文
Unmanned aerial vehicles (UAVs) are capable of serving as aerial base stations (BSs) for providing both cost-effective and on-demand wireless communications. This article investigates dynamic resource allocation of multiple UAVs enabled communication networks with the goal of maximizing long-term rewards. More particularly, each UAV communicates with a ground user by automatically selecting its communicating user, power level and subchannel without any information exchange among UAVs. To model the dynamics and uncertainty in environments, we formulate the long-term resource allocation problem as a stochastic game for maximizing the expected rewards, where each UAV becomes a learning agent and each resource allocation solution corresponds to an action taken by the UAVs. Afterwards, we develop a multi-agent reinforcement learning (MARL) framework that each agent discovers its best strategy according to its local observations using learning. More specifically, we propose an agent-independent method, for which all agents conduct a decision algorithm independently but share a common structure based on Q-learning. Finally, simulation results reveal that: 1) appropriate parameters for exploitation and exploration are capable of enhancing the performance of the proposed MARL based resource allocation algorithm; 2) the proposed MARL algorithm provides acceptable performance compared to the case with complete information exchanges among UAVs. By doing so, it strikes a good tradeoff between performance gains and information exchange overheads.
Keyword:
Resource management
Trajectory
Wireless communication
Communication networks
Dynamic scheduling
Stochastic processes
Reinforcement learning
Dynamic resource allocation
multi-agent reinforcement learning (MARL)
stochastic games
UAV communications
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期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
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