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Mushroomrl : simplifying reinforcement learning research

D'Eramo, Carlo ; Tateo, Davide LU orcid ; Bonarini, Andrea ; Restelli, Marcello and Peters, Jan (2021) In Journal of Machine Learning Research 22.
Abstract

MushroomRL is an open-source Python library developed to simplify the process of im- plementing and running Reinforcement Learning (RL) experiments. Compared to other available libraries, MushroomRL has been created with the purpose of providing a com- prehensive and exible framework to minimize the effort in implementing and testing novel RL methodologies. The architecture of MushroomRL is built in such a way that every component of a typical RL experiment is already provided, and most of the time users can only focus on the implementation of their own algorithms. MushroomRL is accom- panied by a benchmarking suite collecting experimental results of state-of-the-art deep RL algorithms, and allowing to benchmark new ones. The result is... (More)

MushroomRL is an open-source Python library developed to simplify the process of im- plementing and running Reinforcement Learning (RL) experiments. Compared to other available libraries, MushroomRL has been created with the purpose of providing a com- prehensive and exible framework to minimize the effort in implementing and testing novel RL methodologies. The architecture of MushroomRL is built in such a way that every component of a typical RL experiment is already provided, and most of the time users can only focus on the implementation of their own algorithms. MushroomRL is accom- panied by a benchmarking suite collecting experimental results of state-of-the-art deep RL algorithms, and allowing to benchmark new ones. The result is a library from which RL researchers can significantly benefit in the critical phase of the empirical analysis of their works. MushroomRL stable code, tutorials, and documentation can be found at https://github.com/MushroomRL/mushroom-rl.

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author
; ; ; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Benchmarking, Open-source, Python, Reinforcement learning
in
Journal of Machine Learning Research
volume
22
article number
A2
publisher
Microtome Publishing
external identifiers
  • scopus:85112427754
ISSN
1532-4435
language
English
LU publication?
no
additional info
Publisher Copyright: © 2021 Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli and Jan Peters.
id
0bb352ef-da49-4eb5-8a63-a3dfa918758d
date added to LUP
2025-10-16 14:38:42
date last changed
2025-10-22 10:21:03
@article{0bb352ef-da49-4eb5-8a63-a3dfa918758d,
  abstract     = {{<p>MushroomRL is an open-source Python library developed to simplify the process of im- plementing and running Reinforcement Learning (RL) experiments. Compared to other available libraries, MushroomRL has been created with the purpose of providing a com- prehensive and exible framework to minimize the effort in implementing and testing novel RL methodologies. The architecture of MushroomRL is built in such a way that every component of a typical RL experiment is already provided, and most of the time users can only focus on the implementation of their own algorithms. MushroomRL is accom- panied by a benchmarking suite collecting experimental results of state-of-the-art deep RL algorithms, and allowing to benchmark new ones. The result is a library from which RL researchers can significantly benefit in the critical phase of the empirical analysis of their works. MushroomRL stable code, tutorials, and documentation can be found at https://github.com/MushroomRL/mushroom-rl.</p>}},
  author       = {{D'Eramo, Carlo and Tateo, Davide and Bonarini, Andrea and Restelli, Marcello and Peters, Jan}},
  issn         = {{1532-4435}},
  keywords     = {{Benchmarking; Open-source; Python; Reinforcement learning}},
  language     = {{eng}},
  month        = {{06}},
  publisher    = {{Microtome Publishing}},
  series       = {{Journal of Machine Learning Research}},
  title        = {{Mushroomrl : simplifying reinforcement learning research}},
  volume       = {{22}},
  year         = {{2021}},
}