12 citations · 35 across the 14 of their papers we have counts for
14 papers
Reward Machines for Signal Temporal Logic
Alper Kamil Bozkurt, Shangtong Zhang, Yuichi Motai
Signal temporal logic (STL) provides a formal language for specifying real-time properties of real-valued observations, along with a quantitative robustness score for monitoring sa…
Active Offline-to-Online Reinforcement Learning
Alper Kamil Bozkurt, Shangtong Zhang, Yuichi Motai
Background: Offline reinforcement learning (RL) enables effective policies to be trained from large, previously collected datasets and subsequently improved through limited online…
Adaptive Policy Selection and Fine-Tuning under Interaction Budgets for Offline-to-Online Reinforcement Learning
Alper Kamil Bozkurt, Xiaoan Xu, Shangtong Zhang +2
In offline-to-online reinforcement learning (O2O-RL), policies are first safely trained offline using previously collected datasets and then further fine-tuned for tasks via limite…
Neuro-Logic Lifelong Learning
Bowen He, Xiaoan Xu, Alper Kamil Bozkurt +2
Solving Inductive Logic Programming (ILP) problems with neural networks is a key challenge in Neural-Symbolic Ar- tificial Intelligence (AI). While most research has focused on des…
Safe In-Context Reinforcement Learning
Amir Moeini, Minjae Kwon, Alper Kamil Bozkurt +4
In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks without any parameter updates, in…
Accelerated Learning with Linear Temporal Logic using Differentiable Simulation
Alper Kamil Bozkurt, Calin Belta, Ming C. Lin
Ensuring that reinforcement learning (RL) controllers satisfy safety and reliability constraints in real-world settings remains challenging: state-avoidance and constrained Markov…
On the Uniqueness of Solution for the Bellman Equation of LTL Objectives
Zetong Xuan, Alper Kamil Bozkurt, Miroslav Pajic +1
Surrogate rewards for linear temporal logic (LTL) objectives are commonly utilized in planning problems for LTL objectives. In a widely-adopted surrogate reward approach, two disco…
Reinforcement Learning with Temporal Logic Constraints for Partially-Observable Markov Decision Processes
Yu Wang, Alper Kamil Bozkurt, Miroslav Pajic
This paper proposes a reinforcement learning method for controller synthesis of autonomous systems in unknown and partially-observable environments with subjective time-dependent s…
Model-Free Learning of Safe yet Effective Controllers
Alper Kamil Bozkurt, Yu Wang, Miroslav Pajic
We study the problem of learning safe control policies that are also effective; i.e., maximizing the probability of satisfying a linear temporal logic (LTL) specification of a task…
Learning Optimal Strategies for Temporal Tasks in Stochastic Games
Alper Kamil Bozkurt, Yu Wang, Michael M. Zavlanos +1
Synthesis from linear temporal logic (LTL) specifications provides assured controllers for systems operating in stochastic and potentially adversarial environments. Automatic synth…
Secure Planning Against Stealthy Attacks via Model-Free Reinforcement Learning
Alper Kamil Bozkurt, Yu Wang, Miroslav Pajic
We consider the problem of security-aware planning in an unknown stochastic environment, in the presence of attacks on control signals (i.e., actuators) of the robot. We model the…
Model-Free Reinforcement Learning for Stochastic Games with Linear Temporal Logic Objectives
Alper Kamil Bozkurt, Yu Wang, Michael Zavlanos +1
We study the problem of synthesizing control strategies for Linear Temporal Logic (LTL) objectives in unknown environments. We model this problem as a turn-based zero-sum stochasti…
Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning
Alper Kamil Bozkurt, Yu Wang, Michael M. Zavlanos +1
We present a reinforcement learning (RL) framework to synthesize a control policy from a given linear temporal logic (LTL) specification in an unknown stochastic environment that c…
Attack-Resilient Supervisory Control of Discrete-Event Systems: A Finite-State Transducer Approach
Yu Wang, Alper Kamil Bozkurt, Nathan Smith +1
Resilience to sensor and actuator attacks is a major concern in the supervisory control of discrete events in cyber-physical systems (CPS). In this work, we propose a new framework…