MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments

Lucas Nunes Alegre; Florian Felten; El-Ghazali Talbi; Grégoire Danoy; Ann Nowé; Ana Bazzan; Bruno C. da Silva

MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments

Lucas Nunes Alegre, Florian Felten, El-Ghazali Talbi, Grégoire Danoy, Ann Nowé, Ana Bazzan, Bruno C. da Silva

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Research

Original language	English
Title of host publication	Proceedings of the 34th Benelux Conference on Artificial Intelligence BNAIC/Benelearn 2022
Publication status	Published - 2022
Event	BNAIC/BeNeLearn 2022: Joint International Scientific Conferences on AI and Machine Learning - Lamot Mechelen, Belgium Duration: 7 Nov 2022 → 9 Nov 2022 https://bnaic2022.uantwerpen.be/

Conference

Conference	BNAIC/BeNeLearn 2022
Abbreviated title	BNAIC/BeNeLearn 2022
Country/Territory	Belgium
City	Lamot Mechelen
Period	7/11/22 → 9/11/22
Internet address	https://bnaic2022.uantwerpen.be/

Access to Document

https://bnaic2022.uantwerpen.be/wp-content/uploads/BNAICBeNeLearn_2022_submission_6485.pdf

2 Conference paper

Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization
Nunes Alegre, L., Bazzan, A., Roijers, D. M., Nowe, A. & C. da Silva, B., 2023, (Accepted/In press) AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), p. 2003–2012 10 p. (The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023)).
Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Research

Open Access
File
Towards Sample-Efficient Multi-Objective Reinforcement Learning
Nunes Alegre, L., 2023, (Accepted/In press) In: The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023). 3 p.
Research output: Contribution to journal › Conference paper

Open Access
File

Cite this

@inproceedings{ad33212e3ba44953b5a653895d92c2ae,

title = "MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments",

author = "{Nunes Alegre}, Lucas and Florian Felten and El-Ghazali Talbi and Gr{\'e}goire Danoy and Ann Now{\'e} and Ana Bazzan and {C. da Silva}, Bruno",

year = "2022",

language = "English",

booktitle = "Proceedings of the 34th Benelux Conference on Artificial Intelligence BNAIC/Benelearn 2022",

note = "BNAIC/BeNeLearn 2022 : Joint International Scientific Conferences on AI and Machine Learning, BNAIC/BeNeLearn 2022 ; Conference date: 07-11-2022 Through 09-11-2022",

url = "https://bnaic2022.uantwerpen.be/",

}

Nunes Alegre, L, Felten, F, Talbi, E-G, Danoy, G, Nowé, A, Bazzan, A & C. da Silva, B 2022, MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments. in Proceedings of the 34th Benelux Conference on Artificial Intelligence BNAIC/Benelearn 2022. BNAIC/BeNeLearn 2022, Lamot Mechelen, Belgium, 7/11/22. <https://bnaic2022.uantwerpen.be/wp-content/uploads/BNAICBeNeLearn_2022_submission_6485.pdf>

MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments. / Nunes Alegre, Lucas; Felten, Florian; Talbi, El-Ghazali; Danoy, Grégoire; Nowé, Ann; Bazzan, Ana; C. da Silva, Bruno.

Proceedings of the 34th Benelux Conference on Artificial Intelligence BNAIC/Benelearn 2022. 2022.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Research

TY - GEN

T1 - MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments

AU - Nunes Alegre, Lucas

AU - Felten, Florian

AU - Talbi, El-Ghazali

AU - Danoy, Grégoire

AU - Nowé, Ann

AU - Bazzan, Ana

AU - C. da Silva, Bruno

PY - 2022

Y1 - 2022

M3 - Conference paper

BT - Proceedings of the 34th Benelux Conference on Artificial Intelligence BNAIC/Benelearn 2022

T2 - BNAIC/BeNeLearn 2022

Y2 - 7 November 2022 through 9 November 2022

ER -

MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments

Conference

Access to Document

Research output

Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization

Towards Sample-Efficient Multi-Objective Reinforcement Learning

Cite this