Summary of Scilab-rl: a Software Framework For Efficient Reinforcement Learning and Cognitive Modeling Research, by Jan Dohmen et al.
Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research
by Jan Dohmen, Frank Röder, Manfred Eppe
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Scilab-RL, a software framework that aims to streamline research in cognitive modeling and reinforcement learning (RL) for robotic agents. The framework combines various tools, including robotic simulators, data visualization, hyperparameter optimization, and baseline experiments, to reduce the time spent on setting up computational frameworks. Specifically, it focuses on goal-conditioned RL using Stable Baselines 3 and the OpenAI gym interface. This modular suite of tools enables researchers to conduct experiments efficiently, maximizing research output. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scilab-RL is a software framework that helps scientists studying how robots learn and make decisions. It makes it easier for them to test their ideas without wasting time on setting up complicated computer programs. The framework has different parts that work together, including tools for simulating robots, visualizing data, adjusting settings, and comparing results. This means researchers can focus on what they’re really interested in – understanding how robots think and make decisions. |
Keywords
* Artificial intelligence * Hyperparameter * Optimization * Reinforcement learning
