Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911764828913664 |
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| author | Dohmen, Jan Röder, Frank Eppe, Manfred |
| author_facet | Dohmen, Jan Röder, Frank Eppe, Manfred |
| contents | One problem with researching cognitive modeling and reinforcement learning (RL) is that researchers spend too much time on setting up an appropriate computational framework for their experiments. Many open source implementations of current RL algorithms exist, but there is a lack of a modular suite of tools combining different robotic simulators and platforms, data visualization, hyperparameter optimization, and baseline experiments. To address this problem, we present Scilab-RL, a software framework for efficient research in cognitive modeling and reinforcement learning for robotic agents. The framework focuses on goal-conditioned reinforcement learning using Stable Baselines 3 and the OpenAI gym interface. It enables native possibilities for experiment visualizations and hyperparameter optimization. We describe how these features enable researchers to conduct experiments with minimal time effort, thus maximizing research output. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14488 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research Dohmen, Jan Röder, Frank Eppe, Manfred Machine Learning Artificial Intelligence Robotics One problem with researching cognitive modeling and reinforcement learning (RL) is that researchers spend too much time on setting up an appropriate computational framework for their experiments. Many open source implementations of current RL algorithms exist, but there is a lack of a modular suite of tools combining different robotic simulators and platforms, data visualization, hyperparameter optimization, and baseline experiments. To address this problem, we present Scilab-RL, a software framework for efficient research in cognitive modeling and reinforcement learning for robotic agents. The framework focuses on goal-conditioned reinforcement learning using Stable Baselines 3 and the OpenAI gym interface. It enables native possibilities for experiment visualizations and hyperparameter optimization. We describe how these features enable researchers to conduct experiments with minimal time effort, thus maximizing research output. |
| title | Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research |
| topic | Machine Learning Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2401.14488 |