Solving Online Resource-Constrained Scheduling for Follow-Up Observation in Astronomy: a Reinforcement Learning Approach

TL;DR

This paper introduces ROARS, a reinforcement learning method for online scheduling of astronomical observations, effectively handling complex constraints and outperforming traditional heuristics in real-world simulation scenarios.

Contribution

The paper presents a novel reinforcement learning approach, ROARS, for online resource-constrained scheduling in astronomy, capturing schedule dependencies with DAGs and learning effective policies.

Findings

ROARS outperforms five popular heuristics in simulations.

It adapts to various observation scenarios.

It learns effective scheduling strategies with hindsight.

Abstract

In the astronomical observation field, determining the allocation of observation resources of the telescope array and planning follow-up observations for targets of opportunity (ToOs) are indispensable components of astronomical scientific discovery. This problem is computationally challenging, given the online observation setting and the abundance of time-varying factors that can affect whether an observation can be conducted. This paper presents ROARS, a reinforcement learning approach for online astronomical resource-constrained scheduling. To capture the structure of the astronomical observation scheduling, we depict every schedule using a directed acyclic graph (DAG), illustrating the dependency of timing between different observation tasks within the schedule. Deep reinforcement learning is used to learn a policy that can improve the feasible solution by iteratively local rewriting until convergence. It can solve the challenge of obtaining a complete solution directly from scratch in astronomical observation scenarios, due to the high computational complexity resulting from numerous spatial and temporal constraints. A simulation environment is developed based on real-world scenarios for experiments, to evaluate the effectiveness of our proposed scheduling approach. The experimental results show that ROARS surpasses 5 popular heuristics, adapts to various observation scenarios and learns effective strategies with hindsight.