Neurosymbolic Meta-Reinforcement Lookahead Learning Achieves Safe Self-Driving in Non-Stationary Environments
On the map: End-to-end autonomous driving → Reinforcement learning for driving
Abstract (arXiv)
In the area of learning-driven artificial intelligence advancement, the integration of machine learning (ML) into self-driving (SD) technology stands as an impressive engineering feat. Yet, in real-world applications outside the confines of controlled laboratory scenarios, the deployment of self-driving technology assumes a life-critical role, necessitating heightened attention from researchers towards both safety and efficiency. To illustrate, when a self-driving model encounters an unfamiliar environment in real-time execution, the focus must not solely revolve around enhancing its anticipated performance; equal consideration must be given to ensuring its execution or real-time adaptation maintains a requisite level of safety. This study introduces an algorithm for online meta-reinforcement learning, employing lookahead symbolic constraints based on Neurosymbolic Meta-Reinforcement Lookahead Learning (NUMERLA). NUMERLA proposes a lookahead updating mechanism that harmonizes the efficiency of online adaptations with the overarching goal of ensuring long-term safety. Experimental results demonstrate NUMERLA confers the self-driving agent with the capacity for real-time adaptability, leading to safe and self-adaptive driving under non-stationary urban human-vehicle interaction scenarios.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 13 of its 20 references that are in the map. First posted on arXiv 5 Sep 2023.
Builds on
13 of 20 references.
Closest ancestors · 1
Same direction, newest first
- 2022Self-Adaptive Driving in Nonstationary Environments through Conjectural Online Lookahead Adaptation6 Oct
Other references · 12
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
- 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
- 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
- 2017Safe Reinforcement Learning via Shieldingcited by 427
- 2019Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variablescited by 372
- 2018Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learningcited by 270
- 2019Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learningcited by 53
- 2019Continuous Meta-Learning without Taskscited by 50
- 2020Neurosymbolic Reinforcement Learning with Formally Verified Explorationcited by 49
- 2021The Confluence of Networks, Games and Learningcited by 26
- 2019On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learningcited by 12
- 2020Blackwell Online Learning for Markov Decision Processescited by 12
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Citing papers per year: 2025: 1.
1 paper.
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Close in wording, no citation either way
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- 2026Distill to Think, Foresee to Act: Cognitive-Physical Reinforcement Learning for Autonomous Driving
- 2024A Safety-Oriented Self-Learning Algorithm for Autonomous Driving: Evolution Starting from a Basic Model
- 2023Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving
- 2020Safe Trajectory Planning Using Reinforcement Learning for Self Driving
- 2023Robust Driving Policy Learning with Guided Meta Reinforcement Learning
- 2024LoRD: Adapting Differentiable Driving Policies to Distribution Shifts