… optimize the hyperparameters of RL algorithms using existing methods. We compare our
parallel Bayesian optimization … can be leveraged to better optimize RL hyperparameters. …

… ’ trajectories are further optimized by using multiagent deep reinforcement learning without
… To improve the learning efficiency, we further employ Bayesian optimization to estimate the …

… ) for Bayesian optimization, staying as close to a truly Bayesian treatment as … Bayesian
optimization with 21 tasks, parallel optimization of deep neural networks and deep reinforcement …

… In this paper, we present a Bayesian optimization approach for tuning algorithms where
iterative learning is available – the cases of deep learning and deep reinforcement learning. First…

… Bayesian optimization has demonstrated impressive success in finding the optimum input
x… We demonstrate real applications in tuning a deep reinforcement learning algorithm on the …

… This contribution uses a modern Deep Reinforcement Learning (DRL) energy management,
… to optimize controls for real stochastic vehicle use. Additionally, a Bayesian Optimization …

… This paper presents an IRL framework called Bayesian optimization-IRL (BO-IRL) which …
BO-IRL achieves this by utilizing Bayesian Optimization along with our newly proposed …

… inspection of the reinforced concrete bridge using deep learning with … This article, based
on Bayesian optimization, proposes a … are observed on all three-level deep learning models. …

… Bayesian optimization and Gaussian process regression to optimize the hyper-parameters of
a reinforcement … Terms—autonomous reinforcement learning, hyperparameter optimization, …

… Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a
stochastic actor. In International Conference on Machine Learning, pages 1861–1870, 2018. …