denisyarats/exorl
ExORL: Exploratory Data for Offline Reinforcement Learning
This project helps robotics researchers and practitioners evaluate and improve offline reinforcement learning algorithms. It provides a set of pre-recorded exploratory datasets from various simulated robotic environments and allows you to test how different offline reinforcement learning methods perform on them. The output helps you understand which algorithms are most effective given specific exploratory data.
129 stars. No commits in the last 6 months.
Use this if you are developing or testing offline reinforcement learning models for robotics and want to evaluate their performance against diverse, pre-collected exploratory datasets without needing to run real-world or extensive simulation data collection.
Not ideal if you are looking for a general-purpose reinforcement learning framework for online training or if your primary interest is in designing new data collection strategies rather than evaluating existing datasets.
Stars
129
Forks
9
Language
Python
License
MIT
Category
Last pushed
Feb 08, 2022
Commits (30d)
0
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