nrontsis/PILCO
Bayesian Reinforcement Learning in Tensorflow
This project helps robotics engineers and control system designers create optimal control policies for physical systems with minimal real-world trials. By providing data from system interactions, it outputs a robust control strategy that accounts for uncertainty. This is ideal for those developing autonomous agents or robotic systems.
335 stars. No commits in the last 6 months.
Use this if you need to develop efficient control policies for dynamic systems, especially when physical experimentation is costly or time-consuming.
Not ideal if you're looking for a general-purpose machine learning library or if your system's dynamics are perfectly known and deterministic.
Stars
335
Forks
83
Language
Python
License
MIT
Category
Last pushed
Feb 15, 2021
Commits (30d)
0
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