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Found 5 papers, 1 papers with code
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.
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.
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.
