Bayesian Ensembles for Exploration in Deep Reinforcement Learning; Code underlying the dissertation "Bayesian Model-Free Deep Reinforcement Learning"

DOI:10.4121/2dcc8aeb-fdbb-4455-add2-c55c5afcc5d4.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/2dcc8aeb-fdbb-4455-add2-c55c5afcc5d4

Datacite citation style

van der Vaart, Pascal (2025): Bayesian Ensembles for Exploration in Deep Reinforcement Learning; Code underlying the dissertation "Bayesian Model-Free Deep Reinforcement Learning". Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/2dcc8aeb-fdbb-4455-add2-c55c5afcc5d4.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Software

Code underlying the dissertation "Bayesian Model-Free Deep Reinforcement Learning". This repository contains code for the chapter "Bayesian Ensembles for Exploration in Deep Q-Learning". The code is for experiments which demonstrate how ensemble-based Bayesian methods can improve exploration efficiency in reinforcement learning, with a focus on the DQN architecture. The code can be used to reproduce the results and be modified by researchers to be applied to other benchmark problems. The code has no direct applications outside of reinforcement learning research.

History

  • 2025-08-11 first online, published, posted

Publisher

4TU.ResearchData

Format

.py

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems

To access the source code, use the following command:

git clone https://data.4tu.nl/v3/datasets/3a105521-4def-4d63-a350-5d3b4cadd35b.git

Or download the latest commit as a ZIP.