Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Yazicioglu, Yasin"'
Constrained Reinforcement Learning (CRL) is a subset of machine learning that introduces constraints into the traditional reinforcement learning (RL) framework. Unlike conventional RL which aims solely to maximize cumulative rewards, CRL incorporates
Externí odkaz:
http://arxiv.org/abs/2410.08022
We propose an automata-theoretic approach for reinforcement learning (RL) under complex spatio-temporal constraints with time windows. The problem is formulated using a Markov decision process under a bounded temporal logic constraint. Different from
Externí odkaz:
http://arxiv.org/abs/2307.15910
Learning in games has been widely used to solve many cooperative multi-agent problems such as coverage control, consensus, self-reconfiguration or vehicle-target assignment. One standard approach in this domain is to formulate the problem as a potent
Externí odkaz:
http://arxiv.org/abs/2209.02617
We propose control barrier functions (CBFs) for a family of dynamical systems to satisfy a broad fragment of Signal Temporal Logic (STL) specifications, which may include subtasks with nested temporal operators or conflicting requirements (e.g., achi
Externí odkaz:
http://arxiv.org/abs/2204.03631
We study distributed planning for multi-robot systems to provide optimal service to cooperative tasks that are distributed over space and time. Each task requires service by sufficiently many robots at the specified location within the specified time
Externí odkaz:
http://arxiv.org/abs/2107.08540
Publikováno v:
In Robotics and Autonomous Systems June 2024 176
Autor:
Yazicioglu, Yasin, Speranzon, Alberto
We investigate robust linear consensus over networks under capacity-constrained communication. The capacity of each edge is encoded as an upper bound on the number of state variables that can be communicated instantaneously. When the edge capacities
Externí odkaz:
http://arxiv.org/abs/2105.10823
In this paper, we study the maximum edge augmentation problem in directed Laplacian networks to improve their robustness while preserving lower bounds on their strong structural controllability (SSC). Since adding edges could adversely impact network
Externí odkaz:
http://arxiv.org/abs/2105.06011
Control Synthesis using Signal Temporal Logic Specifications with Integral and Derivative Predicates
In many applications, the integrals and derivatives of signals carry valuable information (e.g., cumulative success over a time window, the rate of change) regarding the behavior of the underlying system. In this paper, we extend the expressiveness o
Externí odkaz:
http://arxiv.org/abs/2103.14193
We propose a novel constrained reinforcement learning method for finding optimal policies in Markov Decision Processes while satisfying temporal logic constraints with a desired probability throughout the learning process. An automata-theoretic appro
Externí odkaz:
http://arxiv.org/abs/2102.10063