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Reinforcement Learning aims to train autonomous agents in their interaction with the environment by means of maximizing a given reward signal; in the last decade there has been an explosion of new algorithms, which make extensive use of hyper-parameters to control their behaviour, accuracy and speed. Often those hyper-parameters are fine-tuned by hand, and the selected values may change drastically the learning performance of the algorithm; furthermore, it happens to train multiple agents on very similar problems, starting from scratch each time. Our goal is to design a Meta-Reinforcement Learning algorithm to optimize the hyper-parameter of a well-known RL algorithm, named Trust Region Policy Optimization. We use knowledge from previous le...
In this thesis, we discuss meta learning for control:policy learning algorithms that can themselves ...
We propose a method for meta-learning reinforcement learning algorithms by searching over the space ...
While reinforcement learning (RL) algorithms have been successfully applied to a wide range of probl...
Reinforcement Learning aims to train autonomous agents in their interaction with the environment by ...
Trust region policy optimization (TRPO) is a popular and empirically successful policy search algori...
© 2019, Springer Nature Switzerland AG. In the last few years, we have witnessed a resurgence of int...
In this article, we describe a method for optimizing control policies, with guaran-teed monotonic im...
In this article, we describe a method for optimiz-ing control policies, with guaranteed monotonic im...
In the last few years, we have witnessed a resurgence of interest in neural networks. The state-of-t...
Reinforcement Learning (RL) is a way to train artificial agents to autonomously interact with the wo...
Modern deep reinforcement learning (RL) algorithms are motivated by either the generalised policy it...
International audienceThis paper addresses a cornerstone of Automated Machine Learning: the problem ...
Reinforcement learning (RL) focuses on an essential aspect of intelligent behavior – how an agent ca...
In order for reinforcement learning techniques to be useful in real-world decision making processes,...
Reinforcement learning is a family of machine learning algorithms, in which the system learns to mak...
In this thesis, we discuss meta learning for control:policy learning algorithms that can themselves ...
We propose a method for meta-learning reinforcement learning algorithms by searching over the space ...
While reinforcement learning (RL) algorithms have been successfully applied to a wide range of probl...
Reinforcement Learning aims to train autonomous agents in their interaction with the environment by ...
Trust region policy optimization (TRPO) is a popular and empirically successful policy search algori...
© 2019, Springer Nature Switzerland AG. In the last few years, we have witnessed a resurgence of int...
In this article, we describe a method for optimizing control policies, with guaran-teed monotonic im...
In this article, we describe a method for optimiz-ing control policies, with guaranteed monotonic im...
In the last few years, we have witnessed a resurgence of interest in neural networks. The state-of-t...
Reinforcement Learning (RL) is a way to train artificial agents to autonomously interact with the wo...
Modern deep reinforcement learning (RL) algorithms are motivated by either the generalised policy it...
International audienceThis paper addresses a cornerstone of Automated Machine Learning: the problem ...
Reinforcement learning (RL) focuses on an essential aspect of intelligent behavior – how an agent ca...
In order for reinforcement learning techniques to be useful in real-world decision making processes,...
Reinforcement learning is a family of machine learning algorithms, in which the system learns to mak...
In this thesis, we discuss meta learning for control:policy learning algorithms that can themselves ...
We propose a method for meta-learning reinforcement learning algorithms by searching over the space ...
While reinforcement learning (RL) algorithms have been successfully applied to a wide range of probl...
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