mlsanityData to 14 Oct 2026

Shielded Deep Reinforcement Learning for Complex Spacecraft Tasking

arXiv 2403.05693 ↗First posted 8 Mar 2024cs.LGcited by 2

On the map: Deep reinforcement learning methods → Safe and constrained RL

Abstract (arXiv)

Autonomous spacecraft control via Shielded Deep Reinforcement Learning (SDRL) has become a rapidly growing research area. However, the construction of shields and the definition of tasking remains informal, resulting in policies with no guarantees on safety and ambiguous goals for the RL agent. In this paper, we first explore the use of formal languages, namely Linear Temporal Logic (LTL), to formalize spacecraft tasks and safety requirements. We then define a manner in which to construct a reward function from a co-safe LTL specification automatically for effective training in SDRL framework. We also investigate methods for constructing a shield from a safe LTL specification for spacecraft applications and propose three designs that provide probabilistic guarantees. We show how these shields interact with different policies and the flexibility of the reward structure through several experiments.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 20 references that are in the map. First posted on arXiv 8 Mar 2024.

Builds on

6 of 20 references.

References · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  3. 2017Safe Reinforcement Learning via Shieldingcited by 449
  4. 2015Shield Synthesis: Runtime Enforcement for Reactive Systemscited by 42
  5. 2015Safety-Constrained Reinforcement Learning for MDPscited by 33
  6. 2015Correct-by-synthesis reinforcement learning with temporal logic constraintscited by 11

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Learning-Based Shielding for Safe Autonomy under Unknown Dynamicscited by 3
  2. 2025Follow the STARs: Dynamic ω-Regular Shielding of Learned Policiescited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL
  2. 2026Shielded Reinforcement Learning Under Dynamic Temporal Logic Constraints
  3. 2024Compositional Shielding and Reinforcement Learning for Multi-Agent Systems
  4. 2025Accelerated Learning with Linear Temporal Logic using Differentiable Simulation
  5. 2024Comp-LTL: Temporal Logic Planning via Zero-Shot Policy Composition
  6. 2024Realizable Continuous-Space Shields for Safe Reinforcement Learning
  7. 2025Adaptive GR(1) Specification Repair for Liveness-Preserving Shielding in Reinforcement Learning
  8. 2024Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement Learning