![Institutional Repository [SANDBOX]](images/dias.logo.png "Institutional Repository [SANDBOX]"){kind=logo}
Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/C2182ACC-1A55-48C2-8366-8094FE4CD43B | ||
| Year | 2025 | ||
| Type of Item | Diploma Work | ||
| License |
|
||
| Bibliographic Citation | Andreas Kallinteris, " Building Configurable Reinforcement Learning Robotic Environments", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2025 https://doi.org/10.26233/heallink.tuc.102570 | ||
| Appears in Collections |
The creation of standardized environment implementations and an Application Programming Interface (API) for OpenAI/Gym has had a transformative impact on reinforcement learning (RL) research. However, the current set of standardized environments has to be extended, so as to contribute to the further advancement of reinforcement learning algorithms. In this diploma thesis, we have developed, and we provide a plethora of novel environments and frameworks for robotic reinforcement learning, including Gymnasium/Mujoco-v5, Gymnasium-Robotics/Maze-v5, and Gymnasium-Robotics /MaMuJoCo, along with offline RL datasets for Gymnasium/MuJoCo environments with the Minari API. These advancements can potentially enable researchers to develop and test new algorithms in more realistic and challenging environments, which will ultimately lead to more robust and generalizable reinforcement learning algorithms.
Copyright © DIAS 2013
