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This paper introduces a Deep Recurrent Reinforcement Learning (DRL) framework using a CNN-LSTM architecture for dynamic scheduling of radio telescopes to mitigate radio frequency interference (RFI). The DRL agent learns to control the KMITL radio telescope in a custom simulation environment, optimizing for survey coverage while avoiding RFI. Results show the recurrent DRL agent achieves a 72.7% improvement in mean effective survey coverage compared to a non-recurrent baseline, with minimal performance degradation in real-world deployment.
Recurrent reinforcement learning boosts radio telescope survey coverage by 73% by dynamically avoiding radio interference, proving its viability for real-world astronomical observation.
The proliferation of terrestrial and space-based communication systems introduces significant radio frequency interference (RFI), which severely compromises data acquisition for radio telescopes, necessitating robust and dynamic scheduling solutions. This study addresses this challenge by implementing a Deep Recurrent Reinforcement Learning (DRL) framework for the control and dynamic scheduling of the X-Y pedestal-mounted KMITL radio telescope, explicitly trained for RFI avoidance. The methodology involved developing a custom simulation environment with a domain-specific Convolutional Neural Network (CNN) feature extractor and a Long Short-Term Memory (LSTM) network to model temporal dynamics and long-horizon planning. Comparative evaluation demonstrated that the recurrent DRL agent achieved a mean effective survey coverage of 475 deg2/h, representing a 72.7% superiority over the non-recurrent baseline, and maintained exceptional stability with only 1.0% degradation in median coverage during real-world deployment. The DRL framework offers a highly reliable and adaptive solution for telescope scheduling that is capable of maintaining survey efficiency while proactively managing dynamic RFI sources.