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

… On many Atari console games, reinforcement learning (RL) algorithms deliver substantially
… In Section 6, we augment our analysis with empirical findings on different tasks and learning …

… An empirical analysis of value function-based and policy search reinforcement learning.
AAMAS ’09: Proceedings of the 8th international conference on Autonomous agents and …

… in reinforcement learning using domain knowledge and knowledge-based approaches to
reinforcement learning. … Theoretical and empirical analysis of reward shaping in reinforcement …

… But we show that with them one can construct a simple policylearning algorithm. The …
empirical investigation showing that a conventional reinforcement learning algorithm (Qlearning …

… 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 …

… 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…

… To identify the related literature, we used the search string TITLE-ABS("reinforcement
learning") AND TITLE(learning) AND [TITLE(safe*) OR TITLE(verif*) OR TITLE(formal*) OR TITLE(…