MO-Gym
Library for multi-objective reinforcement learning environments
Description:
MO-Gym is an extensible library containing a diverse set of multi-objective reinforcement learning environments. It introduces a standardized API that facilitates conducting experiments and performance analyses of algorithms designed to interact with multi-objective Markov decision processes. Importantly, it extends the widely-used OpenAI Gym API, allowing the reuse of algorithms and features that are well-established in the reinforcement learning community.
More Information:
- Grand Challenge/Workpackage(s): GC3 - WP2
- Responsible Research Lead: Lucas N. Alegre
- Authors: Lucas N. Alegre, Florian Felten
- Programming Language: Python
- Link to the repository
- Link to video
- Categories: Reinforcement learning
Publications:
- Lucas N. Alegre, Florian Felten, El-Ghazali Talbi, Grégoire Danoy, Ann Nowé, Ana L. C. Bazzan, and Bruno C. da Silva. 2022. MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments. In Proceedings of the 34th Benelux Conference on Artificial Intelligence BNAIC/Benelearn 2022.
