Reinforcement Learning for Telescope Ops

Updated 14 October 2025

Reinforcement Learning for Telescope Operations is a method that applies RL algorithms to automate tasks like scheduling, calibration, and adaptive optics using Markov Decision Processes.
It leverages both model-free and model-based techniques (e.g., A2C, PPO, DQN) to manage high-dimensional, stochastic environments in astronomical observatories.
RL methods improve operational efficiency and scientific output by dynamically optimizing telescope control, reducing errors, and surpassing traditional heuristic approaches.

Reinforcement learning (RL) for telescope operations refers to the application of RL algorithms to optimize and automate various decision-making and control tasks in astronomical observatories. This encompasses scheduling observations, adaptive optics control, alignment, calibration, and resource allocation across ground- and space-based telescopes. RL methods are designed to address the high-dimensional, temporally extended, and stochastic nature inherent to astronomical operations, often surpassing traditional heuristics by adaptively learning policies that maximize scientific yield, operational efficiency, or instrument stability under uncertainty.

1. Foundations and Problem Formulation

In telescope operations, RL frameworks typically model the system as a Markov Decision Process (MDP), defined by a tuple $(S, A, P, R, \gamma)$ :

$S$ represents the state space: the operational status of telescopes, environmental conditions, instrument settings, and/or system histories.
$A$ is the action space: discrete (e.g., target selection, vent open/close) or continuous (e.g., DM voltages, pointing offsets) commands.
$P$ encodes transition dynamics: how the system evolves in response to actions—including physics-based laws, scheduling constraints, and environmental stochasticity.
$R$ is the reward function: a quantitative scalar reflecting scientific utility (e.g., exposure quality, image Strehl ratio, scheduling completion time), cost, or task-specific objectives.
$\gamma$ is the discount factor.

The RL agent observes a state $s_t$ , selects action $a_t$ , transitions to $s_{t+1}$ , and receives reward $r_t$ with the objective to maximize the expected discounted cumulative reward: $\sum_{t=0}^T \gamma^t R(s_t, a_t, s_{t+1})$ . Model formulations and reward definitions are highly task-specific, often incorporating complex dependencies such as target observability, weather forecasts, real-time instrument feedback, and long-term campaign priorities (Hadj-Salah et al., 2019, Terranova et al., 2023, Zhang et al., 16 Feb 2025).

2. Applications and Task-Specific Implementations

Applications of RL in telescope operations fall into several major domains:

a. Scheduling and Campaign Optimization

Observation scheduling employs RL to solve the combinatorial and often multi-objective problem of assigning telescope time to targets under resource, weather, and temporal constraints. Deep RL agents (e.g., A2C, DQN, Rainbow DQN) learn to select optimal targets and timings to minimize observation completion time or maximize total scientific value, adapting to stochastic effects such as weather-induced downtime or competing scientific priorities (Hadj-Salah et al., 2019, Terranova et al., 2023, Zhang et al., 16 Feb 2025).
Resource-constrained online scheduling for follow-up targets (e.g., transients) is framed as MDPs where the schedule is a DAG, and deep RL policies iteratively refine the schedule via local rewrites, outperforming traditional heuristics in average task slowdown and computational efficiency (Zhang et al., 16 Feb 2025).

b. Adaptive Optics (AO) and Wavefront Control

Model-free RL (e.g., DDPG, RDPG with LSTM, PPO) has been used for closed-loop AO control, directly commanding DMs based on wavefront sensor feedback and previous actions. These policies capture temporal correlations, predict disturbances, and outperform integrator controllers by reducing residual RMS error and improving contrast by up to two orders of magnitude, crucial for exoplanet imaging (Landman et al., 2020, Landman et al., 2021, Nousiainen et al., 2023).
Model-based RL (MBRL) frameworks such as PO4AO train NN-based system dynamics models and optimize NN control policies via short-horizon rollouts, enabling predictive control that addresses temporal delays and misregistrations. MBRL approaches demonstrated improvement factors of 3–7 in contrast variance over static integrators in both numerical and laboratory studies (Nousiainen et al., 2022, Nousiainen et al., 2023).

c. Sensor Management and Alignment

RL agents (DDQN) have been deployed for sensor pointing in space situational awareness, learning discrete policies to maximize the number of targets tracked by Earth-based telescopes, leading to lower state uncertainty in EKF-tracked objects (Oakes et al., 2022).
TD3-based RL achieved rapid, high-quality alignment of optical interferometers with continuous action spaces, transferring from simulation to real setups and surpassing human expert performance (Makarenko et al., 2021).

d. Calibration and Data Processing Pipelines

RL methods (TD3, SAC) have been applied for “smart” hyperparameter calibration in radio telescope pipelines, optimizing regularization factors based on influence maps and noise metrics, improving performance and minimizing manual intervention (Yatawatta et al., 2021).
RL can also optimize calibration/model fitting tasks via reward functions linked to information criteria (e.g., AIC) while controlling computational budget (Yatawatta, 16 May 2024).

e. Wavefront Correction from Image Data

Model-free RL (PPO) can directly map phase diversity images to DM commands, providing aberration correction without explicit physical models, achieving Strehl ratios up to 0.99 and exhibiting robustness to varying SNR (Gutierrez et al., 26 Jun 2024).

f. Telescope Pointing and Precision Guiding

Deep RNNs, LSTMs, and GRUs trained on time-series data deliver self-calibrating pointing models, outperforming legacy systems in operational accuracy and survey throughput (Zariski et al., 10 Jul 2024).

3. Algorithmic Approaches and Architectural Considerations

RL for telescope operations utilizes both model-free and model-based algorithms depending on the control or scheduling context. Key approaches include:

Actor–Critic Algorithms (A2C, PPO): Suitable for continuous and hybrid state-action spaces, leveraging synchronous parallel environments for sample efficiency (Hadj-Salah et al., 2019, Narayanan et al., 14 Aug 2025).
Q-learning and DQN Variants: Effective for discrete scheduling and selection tasks; Rainbow DQN and dueling architectures address sample efficiency, stability, and exploration (Terranova et al., 2023).
Policy Gradient with Recurrent/Convolutional Architectures: RNNs (LSTM, GRU) and ConvLSTM handle partial observability, temporal memory, and spatially structured observations or control commands (e.g., high-order DMs) (Landman et al., 2021, Nousiainen et al., 2023).
Model-Based RL/PETS: Ensemble probabilistic NN dynamics models paired with MPC through CEM provide predictive control in physically complex, delayed systems (Nousiainen et al., 2021, Nousiainen et al., 2022, Nousiainen et al., 2023).
Domain-Specific Enhancements: RL policies incorporate domain priors, reward shaping (e.g., exposure time, effective SNR, Strehl ratio), or “hints” (expert heuristics, physics-based models) to improve convergence and performance (Yatawatta, 16 May 2024).

RL architectures and training protocols are adapted for sim-to-real transfer, e.g., through extensive domain randomization, parallel simulation environments, and offline dataset bootstrapping.

4. Performance Evaluation and Experimental Results

RL-based approaches consistently demonstrate superior or competitive performance relative to legacy expert heuristics, static integrators, and classical scheduling in a variety of telescope operation contexts:

Application Domain	RL Algorithm	Improvement Metric	Reference
EO Sat Scheduling	A2C-20	5.1% reduction in mission length over heuristic	(Hadj-Salah et al., 2019)
AO Tip-Tilt	DDPG/RDPG	6× reduction in RMS error (sim.), 2.2× (lab)	(Landman et al., 2020, Landman et al., 2021)
High-Order AO	ConvLSTM + DDPG	2 orders of magnitude better contrast (sim.)	(Landman et al., 2021)
Wavefront Correction	PPO	Achieves SR ~0.99, robust to SNR variation	(Gutierrez et al., 26 Jun 2024)
Smart Calibration	TD3/SAC	Matches grid search in few steps, reduces human tuning	(Yatawatta et al., 2021)
Scheduling	Rainbow DQN	87%±6% of max attainable reward vs. 39%±12% random	(Terranova et al., 2023)
Resource Scheduling	RL (ROARS)	Nearly halves slowdown over heuristics; efficient	(Zhang et al., 16 Feb 2025)
Orbital Planning	A2C	5.8× better reward, 31.5× fewer steps than PPO	(Narayanan et al., 14 Aug 2025)

Results indicate that RL’s ability to learn long-term policies exploiting environment feedback leads to measurable gains in scientific throughput, observation quality, and utilization.

5. Challenges, Adaptation, and Robustness

RL for telescope operations must address specific challenges:

Stochasticity and Partial Observability: Observation scheduling and AO frequently face unpredictable weather and rapid atmospheric evolution; recurrent and model-based architectures help mitigate these.
Reward Design and Safety: Careful shaping and scaling of reward functions (e.g., penalizing unsafe actions, incorporating multi-faceted scientific metrics) are essential.
Sample Efficiency and Training: Offline datasets, domain randomization, and sim-to-real strategies alleviate sample inefficiency inherent in model-free RL, particularly in physical systems (Gutierrez et al., 26 Jun 2024).
Combinatorial Action Spaces: Scheduling over large target sets is addressed by action space discretization, local rewriting (DAG-based), or continuous control over action “windows” (Zhang et al., 16 Feb 2025, Terranova et al., 2023).
Real-Time Constraints: Fast inference (sub-ms), concurrent training, and efficient buffer management meet the requirements for high-speed AO and scheduling loops; for example, PO4AO adds only ~700 microseconds to total system latency (Nousiainen et al., 2023).

6. Prospects and Research Directions

Several frontiers remain open:

Enhanced Simulation Realism: Integrating higher fidelity atmospheric, mechanical, and system models for both training and validation is critical for deployment readiness (Hadj-Salah et al., 2019, Nousiainen et al., 2023).
Multi-Agent and Networked RL: Coordinated control of telescope arrays and sensor networks can be enabled by multi-agent RL techniques, extending single-agent results to distributed systems (Zhang et al., 16 Feb 2025).
Integrated System Control: RL frameworks may be extended to holistic observatory optimization—telescope pointing, AO, calibration, and scheduling—by combining multiple RL agents or hierarchical RL architectures (Yatawatta, 16 May 2024).
Hint-Assisted and Hybrid Approaches: Combining RL with classical heuristics, domain knowledge, and imitation learning offers avenues for improved policy sample efficiency and interpretability (Yatawatta, 16 May 2024, Breitfeld et al., 5 Sep 2025).
Open-Source Deployment and Reproducibility: Publication of source code and simulation environments is accelerating method dissemination and facilitating field trials in on-sky settings (Nousiainen et al., 2023, Terranova et al., 2023).

7. Significance in the Context of Modern Astronomy

RL stands out as a unifying paradigm to automate complex, multi-objective, and uncertain aspects of telescope operations, ranging from real-time adaptive optics to campaign scheduling and system-wide resource management. The approach’s demonstrated adaptability, performance gains, and ability to generalize position it as a critical technology for optimizing utilization and scientific return in current and next-generation astronomical facilities.