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pro vyhledávání: '"Bozkurt, Alper"'
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 discount factors are used to ensure that the expected return approximates t
Externí odkaz:
http://arxiv.org/abs/2404.05074
Autor:
Holder, Timothy, Rahman, Mushfiqur, Summers, Emily, Roberts, David, Wong, Chau-Wai, Bozkurt, Alper
Animal Assisted Interventions (AAIs) involve pleasant interactions between humans and animals and can potentially benefit both types of participants. Research in this field may help to uncover universal insights about cross-species bonding, dynamic a
Externí odkaz:
http://arxiv.org/abs/2211.03636
This paper proposes a reinforcement learning method for controller synthesis of autonomous systems in unknown and partially-observable environments with subjective time-dependent safety constraints. Mathematically, we model the system dynamics by a p
Externí odkaz:
http://arxiv.org/abs/2104.01612
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, and the discounted reward capturing the (classic) control performanc
Externí odkaz:
http://arxiv.org/abs/2103.14600
Synthesis from linear temporal logic (LTL) specifications provides assured controllers for systems operating in stochastic and potentially adversarial environments. Automatic synthesis tools, however, require a model of the environment to construct c
Externí odkaz:
http://arxiv.org/abs/2102.04307
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 attacker as an agent who has the full knowledge of the controller as w
Externí odkaz:
http://arxiv.org/abs/2011.01882
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 stochastic game between the controller and the environment, where the transitio
Externí odkaz:
http://arxiv.org/abs/2010.01050
Akademický článek
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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 can be modeled as a Markov Decision Process (MDP). Specifically, we lea
Externí odkaz:
http://arxiv.org/abs/1909.07299
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 to design supervisors for CPS under attacks using finite-state transd
Externí odkaz:
http://arxiv.org/abs/1904.03264