Add to ORKGIn this thesis, Gaussian Process based Bayesian Optimization (BO) is applied to Deep Reinforcement Learning Problems, specifically, this document details the application of BO to find the best hyperparameter configuration for the Proximal Policy Optimization algorithm in the DRL problems provided by the gym Python library called Cart Pole and Lunar Lander
Robotic systems often have tunable parameters which can affect performance; Bayesian optimization me...
Reinforcement learning is a machine learning technique in which an artificial intelligence agent is ...
Automatically searching for optimal hyperparameter configurations is of crucial importance for apply...
This thesis addresses many open challenges in hyperparameter tuning of machine learning algorithms. ...
With the increase of machine learning usage by industries and scientific communities in a variety of...
The use of machine learning algorithms frequently involves careful tuning of learning parameters and...
The use of machine learning algorithms frequently involves careful tuning of learning parameters and...
Recently, Bayesian Optimization (BO) has been used to successfully optimize parametric policies in s...
Deep neural networks have recently become astonishingly successful at many machine learning problems...
The goal of this thesis was to implement a practical tool for optimizing hy- perparameters of neural...
Bayesian optimization has recently emerged in the machine learning community as a very effective aut...
Hyperparameter optimization in machine learning is a critical task that aims to find the hyper-param...
We propose an algorithm for a family of optimization problems where the objective can be decomposed ...
In the field of reinforcement learning, how to balance the relationship between exploration and expl...
Abstract. In many Reinforcement Learning (RL) domains there is a high cost for generating experience...
Robotic systems often have tunable parameters which can affect performance; Bayesian optimization me...
Reinforcement learning is a machine learning technique in which an artificial intelligence agent is ...
Automatically searching for optimal hyperparameter configurations is of crucial importance for apply...
This thesis addresses many open challenges in hyperparameter tuning of machine learning algorithms. ...
With the increase of machine learning usage by industries and scientific communities in a variety of...
The use of machine learning algorithms frequently involves careful tuning of learning parameters and...
The use of machine learning algorithms frequently involves careful tuning of learning parameters and...
Recently, Bayesian Optimization (BO) has been used to successfully optimize parametric policies in s...
Deep neural networks have recently become astonishingly successful at many machine learning problems...
The goal of this thesis was to implement a practical tool for optimizing hy- perparameters of neural...
Bayesian optimization has recently emerged in the machine learning community as a very effective aut...
Hyperparameter optimization in machine learning is a critical task that aims to find the hyper-param...
We propose an algorithm for a family of optimization problems where the objective can be decomposed ...
In the field of reinforcement learning, how to balance the relationship between exploration and expl...
Abstract. In many Reinforcement Learning (RL) domains there is a high cost for generating experience...
Robotic systems often have tunable parameters which can affect performance; Bayesian optimization me...
Reinforcement learning is a machine learning technique in which an artificial intelligence agent is ...
Automatically searching for optimal hyperparameter configurations is of crucial importance for apply...