SMR: Scheduler with Multi-Channel Map-Encoded Reinforcement Learning for Radio Telescopes

Abstract

Observation scheduling for large single-dish radio telescopes is a multi-objective optimization problem: schedulers must maximize on-source scientific return under strict mechanical and environmental constraints. Previous dynamic scheduling relies on expert-designed heuristics, while existing reinforcement-learning (RL) approaches often struggle with variable-length target lists and lack an intrinsic representation of sky geometry. We present SMR (Scheduler with Map-encoded Reinforcement Learning), which projects discrete targets onto an azimuth--elevation (Az--El) grid in the local horizon frame. The resulting aligned multi-channel sky maps encode target attributes together with direction-dependent cues such as satellite-interference risk and elevation-dependent receiver gain. This representation provides an explicit spatial inductive bias and enables SMR to learn directly from the sky state. Simulations based on real catalogs and site parameters show that, compared with a tuned look-ahead greedy baseline, SMR achieves about a 10\% relative improvement in time utilization by learning non-myopic scheduling strategies. In the full three-channel setting, SMR further achieves joint trade-off among efficiency, interference avoidance, and observation quality, with up to 17\% higher LIER and 54\% higher HGOR relative to an MLP baseline while maintaining higher utilization across both 12 h and 24 h horizons. Overall, SMR provides a simple and extensible way for data-driven single-dish scheduling.