Search Results for author: Florian Felten

Found 5 papers, 5 papers with code

Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning

1 code implementation • 5 Feb 2024 • Shengyi Huang, Quentin Gallouédec, Florian Felten, Antonin Raffin, Rousslan Fernand Julien Dossa, Yanxiao Zhao, Ryan Sullivan, Viktor Makoviychuk, Denys Makoviichuk, Mohamad H. Danesh, Cyril Roumégous, Jiayi Weng, Chufan Chen, Md Masudur Rahman, João G. M. Araújo, Guorui Quan, Daniel Tan, Timo Klein, Rujikorn Charakorn, Mark Towers, Yann Berthelot, Kinal Mehta, Dipam Chakraborty, Arjun KG, Valentin Charraut, Chang Ye, Zichen Liu, Lucas N. Alegre, Alexander Nikulin, Xiao Hu, Tianlin Liu, Jongwook Choi, Brent Yi

As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone.

reinforcement-learning Reinforcement Learning (RL)

163

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Code

Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework

1 code implementation • 21 Nov 2023 • Florian Felten, El-Ghazali Talbi, Grégoire Danoy

To tackle such an issue, this paper introduces multi-objective reinforcement learning based on decomposition (MORL/D), a novel methodology bridging the literature of RL and MOO.

Multi-Objective Reinforcement Learning reinforcement-learning

220

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Hyperparameter Optimization for Multi-Objective Reinforcement Learning

1 code implementation • 25 Oct 2023 • Florian Felten, Daniel Gareev, El-Ghazali Talbi, Grégoire Danoy

Hence, prior research has explored hyperparameter optimization in RL to address this concern.

Hyperparameter Optimization Multi-Objective Reinforcement Learning +1

220

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A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning

2 code implementations • Conference on Neural Information Processing Systems Datasets and Benchmarks Track 2023 • Florian Felten, Lucas N. Alegre, Ann Nowé, Ana L. C. Bazzan, El-Ghazali Talbi, Grégoire Danoy, Bruno C. da Silva

Multi-objective reinforcement learning algorithms (MORL) extend standard reinforcement learning (RL) to scenarios where agents must optimize multiple---potentially conflicting---objectives, each represented by a distinct reward function.

Benchmarking Multi-Objective Reinforcement Learning +1

238

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

2 code implementations • Benelux Conference on Artificial Intelligence BNAIC/BeNeLearn 2022 • Lucas N. Alegre, Florian Felten, El-Ghazali Talbi, Grégoire Danoy, Ann Nowé, Ana L. C. Bazzan, Bruno C. da Silva

We introduce MO-Gym, an extensible library containing a diverse set of multi-objective reinforcement learning environments.

Multi-Objective Reinforcement Learning OpenAI Gym +1

238

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Code

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