Certified Reinforcement Learning with Logic Guidance
Reinforcement Learning (RL) is a widely employed machine learning architecture that has been applied to a variety of control problems. However, applications in safety-critical domains require a systematic and formal approach to specifying requirements as tasks or goals. We propose a model-free RL algorithm that enables the use of Linear Temporal Logic (LTL) to formulate a goal for unknown continuous-state/action Markov Decision Processes (MDPs). The given LTL property is translated into a Limit-Deterministic Generalised Buchi Automaton (LDGBA), which is then used to shape a synchronous reward function on-the-fly. Under certain assumptions, the algorithm is guaranteed to synthesise a control policy whose traces satisfy the LTL specification with maximal probability.
Code (1)
Tasks
Decision MakingDecision Making Under UncertaintyHierarchical Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Safe Reinforcement LearningSimilar Papers 제목 키워드 기반
Certified Guidance for Planning with Deep Generative Models
Deep generative models, such as generative adversarial networks and diffusion models, have recently emerged as powerful tools for planning tasks and behavior synthesis in autonomous systems. Various guidance strategies h…
A Neuro-Symbolic Approach to Strategy Synthesis for Strategic Logics
Reasoning about what agents can achieve through strategic interaction is a core challenge in Multi-Agent Systems (MAS). Logics for strategic ability, such as ATL, provide rigorous methods, but their adoption is often hin…
Luminark: Training-free, Probabilistically-Certified Watermarking for General Vision Generative Models
In this paper, we introduce \emph{Luminark}, a training-free and probabilistically-certified watermarking method for general vision generative models. Our approach is built upon a novel watermark definition that leverage…
Improving Certified Robustness via Statistical Learning with Logical Reasoning
Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently. However, current robustness certification methods are only able to certify under a …
BIG-bench Machine LearningLogical ReasoningCAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified Radius Maximization
Deep reinforcement learning (DRL) has gained widespread adoption in control and decision-making tasks due to its strong performance in dynamic environments. However, DRL agents are vulnerable to noisy observations and ad…
Adversarial RobustnessDeep Reinforcement Learning