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Found 10 papers, 1 papers with code
Learning-based Bounded Synthesis for Semi-MDPs with LTL Specifications
In the product of the SMDP and the deterministic $K$-co-B\"uchi automaton (d$K$cBA) converted from the LTL specification, we learn both the winning region of satisfying the LTL specification and the dynamics therein based on reinforcement learning and Bayesian inference.
Deep reinforcement learning under signal temporal logic constraints using Lagrangian relaxation
Deep reinforcement learning (DRL) has attracted much attention as an approach to solve optimal control problems without mathematical models of systems.
Deep Reinforcement Learning Based Networked Control with Network Delays for Signal Temporal Logic Specifications
We apply deep reinforcement learning (DRL) to design of a networked controller with network delays to complete a temporal control task that is described by a signal temporal logic (STL) formula.
Collaborative rover-copter path planning and exploration with temporal logic specifications based on Bayesian update under uncertain environments
An exploration policy for the copter is then synthesized by employing the notion of an entropy that is evaluated based on the environmental beliefs of the atomic propositions, and a path that the rover intends to follow according to the optimal policy.
Bounded Synthesis and Reinforcement Learning of Supervisors for Stochastic Discrete Event Systems with LTL Specifications
We compute a winning region and a directed controller with the maximum satisfaction probability using a dynamic programming method, where the expected return is used as a value function.
Continuous Deep Q-Learning with Simulator for Stabilization of Uncertain Discrete-Time Systems
If we know a mathematical model of a real system, a simulator is useful because it predicates behaviors of the real system using the mathematical model with a given system parameter vector.
Learning-based Symbolic Abstractions for Nonlinear Control Systems
Symbolic models or abstractions are known to be powerful tools for the control design of cyber-physical systems (CPSs) with logic specifications.
Reinforcement Learning of Control Policy for Linear Temporal Logic Specifications Using Limit-Deterministic Generalized Büchi Automata
This letter proposes a novel reinforcement learning method for the synthesis of a control policy satisfying a control specification described by a linear temporal logic formula.
Networked Control of Nonlinear Systems under Partial Observation Using Continuous Deep Q-Learning
In this paper, we propose a design of a model-free networked controller for a nonlinear plant whose mathematical model is unknown.
Model-free Control of Chaos with Continuous Deep Q-learning
Moreover, model-free reinforcement learning algorithms with deep neural networks have the disadvantage in taking much time to learn their control optimal policies.
