Reinforcement Learning in Astronomy

• Reinforcement Learning in Astronomy is a framework that applies sequential decision-making and MDP formulations to optimize tasks such as telescope scheduling, adaptive optics, and spacecraft trajectory planning.
• RL techniques enhance efficiency in areas like trajectory design and adaptive optics, achieving performance improvements such as a 10–14% reduction in mission time and a threefold wavefront error reduction.
• A variety of methods including model-free, model-based, and hybrid approaches are used to automate calibration, control instruments, and manage multi-agent orbital dynamics, outperforming traditional heuristics.

Reinforcement learning (RL) in astronomy refers to the application of sequential decision-making algorithms that learn adaptive strategies for observing, processing, controlling, and optimizing diverse astronomical systems and problems. Drawing on the Markov decision process (MDP) framework, RL agents interact with simulators or real-world environments to maximize cumulative objective functions tailored to scientific yield, resource use, or operational robustness. The field now encompasses spacecraft guidance, telescope scheduling, calibration workflows in radio astronomy, orbital maneuvering, adaptive optics, planetary rover autonomy, event follow-up coordination, and simulation parameter tuning. RL approaches in astronomy span model-free policy gradient and value-based methods, model-based planning, hybrid hint-augmented agents, and multi-agent frameworks, often leveraging deep neural networks as function approximators.

1. Fundamental Principles and Markov Decision Process Formulation

Modern RL applied to astronomy relies on casting the scientific or operational problem as an MDP, typically specified by state $s$, action $a$, reward $r$, and transition $p(s'|s,a)$. States encode the current environment or system configuration, actions represent available decisions (control commands, scheduling choices, calibration parameters, etc.), and rewards quantify task performance, data quality, resource savings, or scientific return. The agent's goal is to find a policy $\pi$ maximizing the expected cumulative (discounted) reward: $$J = \mathbb{E}_\pi\left[\sum_{k=0}^{N-1}\gamma^k R(s_k, a_k, s_{k+1}) \right].$$ Approaches span discrete and continuous action/state settings (Yatawatta, 16 May 2024). Many astronomical RL systems require careful inclusion of uncertainty (e.g., process and measurement noise, missed thrust events, resource failures), which the MDP must formally encode, either via explicit stochastic transitions or POMDPs for partial observability (Zavoli et al., 2020, Gadgil et al., 2020, Sravan et al., 2023).

2. Trajectory Optimization and Space Mission Planning

RL has been demonstrably successful in robust trajectory design for interplanetary and multi-debris missions. In low-thrust trajectory planning, the state includes position, velocity, and spacecraft mass, with RL recasting the optimal control problem as a time-discrete MDP. The control sequence is encoded as actions subject to physical and mission constraints, e.g., maximum $\Delta V$ per segment dependent on engine properties (Zavoli et al., 2020). PPO-based policies (actor-critic DNNs) trained on stochastic simulators (process/observation/control noise, missed events) produce closed-loop guidance policies that outperform fixed open-loop indirect methods when uncertainties are present, enabling robust constraint satisfaction with only 1–2% loss in payload mass in most cases. MDP formulations such as: $s_{k+1} = f(s_k, u_k, \omega_{s,k}),$ are solved with policy-gradient algorithms (PPO) and generalized advantage estimators.

For multi-debris rendezvous, RL formalism—specifically Masked PPO—handles Combinatorial optimization (analogous to TSP), with invalid action masking ensuring that only unvisited debris can be selected at each step. The neural network policy sequentially constructs optimal visitation ordering, using orbit dynamics solved by fast Lambert solvers (Izzo's adaptation). RL methods attain mission time reductions of 10–14% relative to Genetic and Greedy algorithms with the greatest computational efficiency (Bandyopadhyay et al., 25 Sep 2024).

3. RL for Adaptive Optics and Space Instrument Control

Adaptive optics (AO) control in astronomy is a canonical domain for both model-free and model-based RL. In model-based RL for AO (MBRL), state representations concatenate histories of wavefront sensor (WFS) data and prior deformable mirror (DM) commands, with the agent learning a probabilistic dynamical model $\hat{p}_\omega(s_{t+1}|s_t, a_t)$ and a nonlinear policy. The RL loop minimizes residual wavefront errors via predictive control with rolling-horizon planning: $r(s_t, a_t) = -\| C \Delta w^{t+1} \|^2,$

$$a_t^* = \arg\max_a \mathbb{E}\left[ r(s_t, a_t) + r(s_{t+1}, a_{t+1}) + \cdots \right].$$

On realistic AO test benches, Policy Optimization for AO (PO4AO) achieves a threefold reduction in wavefront error and robust adaptation to delays and misregistration, with performance validated against classical integrator controllers (Nousiainen et al., 2021, Nousiainen et al., 2023). Latency requirements are analyzed—current implementations introduce $\sim$700 µs overhead, with optimization paths including lower-level code and inference accelerators.

RL also achieves effective model-free wavefront control for exoplanet imaging, where an agent receives phase-diversity images and outputs DM corrections to maximize Strehl ratio or minimize dark-hole intensity, using PPO in high-dimensional observation spaces. Rapid correction of aberrations to Strehl >0.99 and significant dark-hole suppression are demonstrated, supporting the potential for data-driven, model-free AO control (Gutierrez et al., 26 Jul 2024).

4. Calibration, Workflow Automation, and Scheduling in Data Pipelines

In radio astronomy, RL methods automate calibration workflow and hyperparameter selection. RL agents, leveraging actor–critic methods (TD3, SAC), tune calibration regularization by using summarized states such as influence maps or residual statistics as input. Reward functions are constructed to balance noise suppression and overfitting (variance in the influence map), while RL agents achieve competitive performance to grid search with greatly reduced evaluations, generalizing to diverse observational conditions (Yatawatta et al., 2021). Hint-assisted RL augments the standard SAC algorithm with inequality constraints that softly enforce proximity between the action and “external hints” derived from established heuristics (e.g., Akaike information criterion for calibration direction selection). Optimized with ADMM, these agents achieve improved sample efficiency and robustness, integrating domain knowledge with learned policy flexibility (Yatawatta, 2023).

RL extends to the automation of full radio interferometry calibration workflows. Q-learning agents are trained to select optimal sequences of data processing actions (e.g., averaging, flagging, calibration) driven by a combined metric: $$\text{Action Value} = \text{Runtime} + (10^6 \times \text{EMD}),$$ where EMD quantifies fidelity to theoretical noise and runtime reflects computational cost. Sequential decision-making (possibly requiring repeated flagging) emerges naturally in learned policies, often achieving or surpassing expert-level choices in simulated datasets (Kirk et al., 22 Oct 2024).

In dynamic scheduling, RL algorithms (value-based Deep Q-Networks, actor–critic rewriting policies with graph encodings) have been applied to telescope campaign optimization (maximizing effective exposure time over competing science objectives) (Terranova et al., 2023), and to the online, resource-constrained scheduling of telescope arrays for follow-up of transients. Here, each schedule is encoded as a directed acyclic graph; DRL agents iteratively improve feasible schedules by local rewriting, minimizing overall task slowdown and outperforming popular heuristics across distributed telescope array scenarios (Zhang et al., 16 Feb 2025).

5. RL for Space Operations and Multi-Agent Orbital Dynamics

High-fidelity orbital simulation environments such as OrbitZoo provide industry-standard propagation (Orekit backend) including all major perturbative effects, supporting RL with state representations in equinoctial elements. RL agents (trained via PPO with Generalized Advantage Estimation) handle both single- and multi-agent tasks. Experiments span Hohmann transfers with continuous-thrust execution, multi-satellite constellation phasing (equal angular separation), and collision avoidance based on real statistical definitions of probability of collision (PoC), with reward structures penalizing fuel expenditure and state deviation. Extensive validation against real Starlink ephemeris demonstrates mean absolute percentage error of 0.16%, supporting deployment as a testbed for RL strategies in satellite safety and constellation management (Oliveira et al., 5 Apr 2025).

6. Scientific Observation Planning, Dynamic Targeting, and Simulation Control

Sophisticated RL frameworks have been developed for optimizing scientific data return in Earth observation satellite missions. In the dynamic targeting context, the problem is formulated as an MDP with state vectors including state-of-charge and compact summaries of lookahead instrument data. Q-learning agents are trained (often in a backward, DP-inspired regime) to maximize total scientific reward—e.g., timely observations of convective storms or clear scenes—subject to resource constraints, outperforming all tested greedy heuristic baselines by substantial margins (by an average of 13.7%). Training converges with moderate data volumes (e.g., $\sim$20,000 images for cloud avoidance) (Breitfeld et al., 5 Sep 2025).

RL has also been used to automate adaptive time-stepping in chaotic gravitational three-body simulations. Here, a DQN agent observes the system state and recent energy error, selecting time-step parameters $\mu$ to dynamically balance error and computational cost throughout the simulation, adapting finely to close encounters and more quiescent epochs. Compared to fixed $\mu$ integration, the RL agent achieves superior tradeoffs without expert tuning, generalizing to multiple variable time-step integrators (Ulibarrena et al., 18 Feb 2025).

7. Outlook and Future Directions

Current RL applications in astronomy have demonstrated: (i) robustness to real-world uncertainties surpassing classical methods in stochastic environments, (ii) the capacity for end-to-end automation of complex workflows, and (iii) adaptability to high-dimensional, non-stationary, or partially observable contexts. Open challenges and prospective avenues include:

• Scaling RL policies for resource allocation and coordination across global instrument networks (Zhang et al., 16 Feb 2025).
• Developing hybrid (multi-agent, hint-augmented, model-based/model-free) architectures for complex astronomical dataflows (Yatawatta, 2023, Oliveira et al., 5 Apr 2025).
• Expanding policy representations to fully leverage deep neural architectures in high-dimensional observation or state spaces (Terranova et al., 2023).
• Deploying RL-trained agents to operate in real time or on board satellites and telescopes for truly autonomous scientific discovery (Breitfeld et al., 5 Sep 2025).
• Systematic benchmarking and open-source infrastructure for reproducibility and cross-comparison (Terranova et al., 2023, Oliveira et al., 5 Apr 2025).

Reinforcement learning is thus positioned as a versatile and increasingly indispensable tool in the automation, optimization, and intelligence augmentation of astronomical systems, able to handle both theoretical control challenges and practical, data-rich operational workflows.