Name
Abstract | ||
|---|---|---|
MushroomRL is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments. Compared to other available libraries, MushroomRL has been created with the purpose of providing a comprehensive and flexible 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 accompanied 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-r1. |
Year | DOI | Venue |
|---|---|---|
2021 | v22/18-056.html | JOURNAL OF MACHINE LEARNING RESEARCH |
Keywords | DocType | Volume |
reinforcement learning, python, open-source, benchmarking | Journal | 22 |
Issue | ISSN | Citations |
1 | 1532-4435 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlo D’Eramo | 1 | 6 | 4.87 |
| Davide Tateo | 2 | 3 | 4.82 |
| Andrea Bonarini | 3 | 623 | 76.73 |
| Marcello Restelli | 4 | 416 | 61.31 |
| Jan Peters | 5 | 3553 | 264.28 |