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Multi-Agent Reinforcement Learning-Based Coverage Maximization for Fixed-Wing Base Stations | IEEE Conference Publication | IEEE Xplore

Multi-Agent Reinforcement Learning-Based Coverage Maximization for Fixed-Wing Base Stations


Abstract:

We propose a trajectory-planning method that utilizes deep Q-network-based multi-agent reinforcement learning architecture for multiple fixed-wing base stations. In our s...Show More

Abstract:

We propose a trajectory-planning method that utilizes deep Q-network-based multi-agent reinforcement learning architecture for multiple fixed-wing base stations. In our system model, the trajectory-planning problem is proposed to maximize the coverage area in the wireless sensor network. For multi-agent learning, we propose an architecture where a single Q-network is shared across all base stations. The result shows that our model can stably converge and almost achieve the performance of a single base station, which is not interrupted by other base stations. Moreover, we propose a penalty function to regulate the base stations when the trajectories are undesirable. The result shows that the penalty function can stabilize the network.
Date of Conference: 19-21 October 2022
Date Added to IEEE Xplore: 25 November 2022
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Conference Location: Jeju Island, Korea, Republic of

References

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