… This paper first reviews the Deep Reinforcement Learning theory and model, validates …
empirical data, and compares the benefits of the three classical Deep Reinforcement Learning …

… We consider the setting in which a reinforcement learning algorithm is evaluated on M
tasks. For each of these tasks, we perform N independent runs3 which each provide a scalar, …

… In this study, a deep Q-network (DQN)-based reinforcement learning (RL) method is
proposed to learn the optimal EV charging strategy considering empirical travel pattern …

… significant advances in reinforcement learning (RL), which has registered tremendous
success in solving various sequential decision-making problems in machine learning. Most of the …

… Our analysis includes 1) the goal of finding top-performing convolutional neural networks
(… propose the reinforcement learning framework for hyperparameter tuning for deep learning-…

… We study model-based reinforcement learning in an unknown finite communicating
Markov decision process. We propose a simple algorithm that leverages a variance based …

… 3 Background: Deep Q-Learning Reinforcement learning deals with learning a policy for
an agent interacting in an unknown environment. At each step, an agent observes the current …

… trast, we believe the value distribution has a central role to play in reinforcement learning. …
In reinforcement learning we are typically interested in acting so as to maximize the return. The …

… , and we believe that by identifying, replicating and solving these challenges, reinforcement
learning can be more readily used to solve many of these important real-world problems. …

… the process of implementing and running Reinforcement Learning (RL) experiments. …
benefit in the critical phase of the empirical analysis of their works. MushroomRL stable code…