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Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments
In this paper, dynamic non-cooperative coexistence between a cognitive pulsed radar and a nearby communications system is addressed by applying nonlinear value function approximation via deep reinforcement learning (Deep RL) to develop a policy for optimal radar performance.
Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar
In this work, we first describe a framework for the application of Reinforcement Learning (RL) control to a radar system that operates in a congested spectral setting.
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