Add to ORKGIn this project we aim to apply Robust Reinforce-ment Learning algorithms, presented by Doya and Morimoto [1],[2], to control problems. Specifically, we train an agent to balancea pendulum in the unstable equilibrium, which is the invertedstate.We investigate the performance of controllers based on twodifferent function approximators. One is quadratic, and the othermakes use of a Radial Basis Function neural network. To achieverobustness we will make use of an approach similar toH∞control, which amounts to introducing an adversary in the controlsystem.By changing the mass of the pendulum after training, we aimedto show as in [2] that the supposedly robust controllers couldhandle this disruption better than its non-robust counterparts.This w...
In this article, we propose a simple, practical, and intuitive approach to improve the performance o...
Reinforcement learning refers to a machine learning paradigm in which an agent interacts with the en...
This work aims at constructing a bridge between robust control theory and reinforcement learning. Al...
In this project we aim to apply Robust Reinforce-ment Learning algorithms, presented by Doya and Mor...
Reinforcement learning emerges as an efficient tool to design control algorithms for nonlinear syste...
Robust control theory is used to design stable con-trollers in the presence of uncertainties. By rep...
In this paper the two reinforcement learning algorithmsQ-learning and deep Q-learning (DQN) are used...
Sim-to-Reality transfer in Reinforcement Learning is a promising approach ofsolving costly explorati...
Department Head: Stephen B. Seidman.2000 Summer.Includes bibliographical references (pages 227-231)....
This paper proposes a robust control design method using reinforcement learning for controlling part...
Reinforcement learning Algorithms such as SARSA, Q learning, Actor-Critic Policy Gradient and Value ...
Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of systems ...
Reinforcement learning is conceptually based on an agent learning through interaction with its envir...
This paper proposes a neural network based reinforcement learning controller that is able to learn c...
Remote Electrical Tilt (RET) is a method for configuring antenna downtilt in base stations to optimi...
In this article, we propose a simple, practical, and intuitive approach to improve the performance o...
Reinforcement learning refers to a machine learning paradigm in which an agent interacts with the en...
This work aims at constructing a bridge between robust control theory and reinforcement learning. Al...
In this project we aim to apply Robust Reinforce-ment Learning algorithms, presented by Doya and Mor...
Reinforcement learning emerges as an efficient tool to design control algorithms for nonlinear syste...
Robust control theory is used to design stable con-trollers in the presence of uncertainties. By rep...
In this paper the two reinforcement learning algorithmsQ-learning and deep Q-learning (DQN) are used...
Sim-to-Reality transfer in Reinforcement Learning is a promising approach ofsolving costly explorati...
Department Head: Stephen B. Seidman.2000 Summer.Includes bibliographical references (pages 227-231)....
This paper proposes a robust control design method using reinforcement learning for controlling part...
Reinforcement learning Algorithms such as SARSA, Q learning, Actor-Critic Policy Gradient and Value ...
Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of systems ...
Reinforcement learning is conceptually based on an agent learning through interaction with its envir...
This paper proposes a neural network based reinforcement learning controller that is able to learn c...
Remote Electrical Tilt (RET) is a method for configuring antenna downtilt in base stations to optimi...
In this article, we propose a simple, practical, and intuitive approach to improve the performance o...
Reinforcement learning refers to a machine learning paradigm in which an agent interacts with the en...
This work aims at constructing a bridge between robust control theory and reinforcement learning. Al...