arXiv subjects

Tiberiu-Andrei Georgescu

Publications and source records attributed to Tiberiu-Andrei Georgescu.

3 recordsLinked to original sources

arXiv2026

Learning to adapt GR(1) specifications through degradation

Reactive synthesis is a powerful tool for generating correct-by-construction controllers from formal specifications. GR(1) is an assume-guarantee specification framework that enables efficient synthesis, allowing synthesised controllers to be used in a wide array of applications. The limitation of such controllers is that, should they encounter environment behaviour unspecified in the assumptions of the specification, the specified system guarantees are no longer ensured. Our work proposes an approach based on oracle-guided inductive synthesis to adapt the specification to be consistent with the observed assumption violation, while degrading system guarantees as little as possible to maintain realisability. Our methodology discovers multiple potential solutions, so we propose a preference criteria, based on the ability of the specification to enable robustness under adaptation. Although our approach is capable of degrading the entire specification, for our case studies we successfully discover degradations that preserve the entire set of original guarantees.

cs.SE↗

arXiv2026

Adaptive GR(1) Specification Repair for Liveness-Preserving Shielding in Reinforcement Learning

Shielding is widely used to enforce safety in reinforcement learning (RL), ensuring that an agent's actions remain compliant with formal specifications. Classical shielding approaches, however, are often static, in the sense that they assume fixed logical specifications and hand-crafted abstractions. While these static shields provide safety under nominal assumptions, they fail to adapt when environment assumptions are violated. In this paper, we develop an adaptive shielding framework based on based on Generalized Reactivity of rank 1 (GR(1)) specifications, a tractable and expressive fragment of Linear Temporal Logic (LTL) that captures both safety and liveness properties. Our method detects environment assumption violations at runtime and employs Inductive Logic Programming (ILP) to automatically repair GR(1) specifications online, in a systematic and interpretable way. This ensures that the shield evolves gracefully, ensuring liveness is achievable and minimally weakening goals only when necessary. We consider two case studies: Minepump and Atari Seaquest; showing that (i) static symbolic controllers are often severely suboptimal when optimizing for auxiliary rewards, and (ii) RL agents equipped with our adaptive shield maintain near-optimal reward and perfect logical compliance compared with static shields.

cs.AI↗

arXiv2022

Transferring Multi-Agent Reinforcement Learning Policies for Autonomous Driving using Sim-to-Real

Autonomous Driving requires high levels of coordination and collaboration between agents. Achieving effective coordination in multi-agent systems is a difficult task that remains largely unresolved. Multi-Agent Reinforcement Learning has arisen as a powerful method to accomplish this task because it considers the interaction between agents and also allows for decentralized training -- which makes it highly scalable. However, transferring policies from simulation to the real world is a big challenge, even for single-agent applications. Multi-agent systems add additional complexities to the Sim-to-Real gap due to agent collaboration and environment synchronization. In this paper, we propose a method to transfer multi-agent autonomous driving policies to the real world. For this, we create a multi-agent environment that imitates the dynamics of the Duckietown multi-robot testbed, and train multi-agent policies using the MAPPO algorithm with different levels of domain randomization. We then transfer the trained policies to the Duckietown testbed and compare the use of the MAPPO algorithm against a traditional rule-based method. We show that the rewards of the transferred policies with MAPPO and domain randomization are, on average, 1.85 times superior to the rule-based method. Moreover, we show that different levels of parameter randomization have a substantial impact on the Sim-to-Real gap.

cs.RO↗