# Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functions

arXiv: https://arxiv.org/abs/1903.09885 · HTML: https://mlsanity.com/paper/1903.09885/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2019.

## Built on by (most cited first, 21 of 21)

- [Constrained Update Projection Approach to Safe Policy Optimization](https://mlsanity.com/paper/2209.07089/) (2022)
- [Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability](https://mlsanity.com/paper/2209.08025/) (2022)
- [A Survey on Physics Informed Reinforcement Learning: Review and Open Problems](https://mlsanity.com/paper/2309.01909/) (2023)
- [Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient Manipulation](https://mlsanity.com/paper/2405.01677/) (2024)
- [A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors](https://mlsanity.com/paper/2302.13137/) (2023)
- [Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2405.16390/) (2024)
- [Reactive and Safe Road User Simulations using Neural Barrier Certificates](https://mlsanity.com/paper/2109.06689/) (2021)
- [Provably Correct Training of Neural Network Controllers Using Reachability Analysis](https://mlsanity.com/paper/2102.10806/) (2021)
- [Model-Free Learning of Safe yet Effective Controllers](https://mlsanity.com/paper/2103.14600/) (2021)
- [A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions](https://mlsanity.com/paper/2508.09128/) (2025)
- [Reinforcement Learning with Formal Performance Metrics for Quadcopter Attitude Control under Non-nominal Contexts](https://mlsanity.com/paper/2107.12942/) (2021)
- [Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation](https://mlsanity.com/paper/2301.11374/) (2023)
- [ConBaT: Control Barrier Transformer for Safe Policy Learning](https://mlsanity.com/paper/2303.04212/) (2023)
- [Learning Adaptive Safety for Multi-Agent Systems](https://mlsanity.com/paper/2309.10657/) (2023)
- [On the Uniqueness of Solution for the Bellman Equation of LTL Objectives](https://mlsanity.com/paper/2404.05074/) (2024)
- [Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis](https://mlsanity.com/paper/2312.11314/) (2023)
- [Shielded Reinforcement Learning Under Dynamic Temporal Logic Constraints](https://mlsanity.com/paper/2603.17152/) (2026)
- [Failing with Grace: Learning Neural Network Controllers that are Boundedly Unsafe](https://mlsanity.com/paper/2106.11881/) (2021)
- [Progressive Safeguards for Safe and Model-Agnostic Reinforcement Learning](https://mlsanity.com/paper/2410.24096/) (2024)
- [Integrating LTL Constraints into PPO for Safe Reinforcement Learning](https://mlsanity.com/paper/2603.01292/) (2026)
- [Kernel-Based Safe Exploration in Deep Reinforcement Learning](https://mlsanity.com/paper/2605.22207/) (2026)

## Abstract (arXiv)

Using reinforcement learning to learn control policies is a challenge when the task is complex with potentially long horizons. Ensuring adequate but safe exploration is also crucial for controlling physical systems. In this paper, we use temporal logic to facilitate specification and learning of complex tasks. We combine temporal logic with control Lyapunov functions to improve exploration. We incorporate control barrier functions to safeguard the exploration and deployment process. We develop a flexible and learnable system that allows users to specify task objectives and constraints in different forms and at various levels. The framework is also able to take advantage of known system dynamics and handle unknown environmental dynamics by integrating model-free learning with model-based planning.