Yuxing-Wang-THU/Surrogate-assisted-ERL
A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning
This tool helps robotics researchers and engineers optimize the control policies for their robots more efficiently, especially when real-world testing is costly or time-consuming. It takes your existing hybrid reinforcement learning and evolutionary algorithm framework as input, and outputs a significantly faster and more stable optimization process for developing robot behaviors. It's designed for anyone working on continuous robot control tasks who faces high costs or long durations for environmental interactions.
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Use this if you are developing control policies for robots using evolutionary reinforcement learning and find the process too slow or expensive due to the need for frequent interactions with the physical environment.
Not ideal if your robot control tasks are simple or inexpensive to simulate, or if you are not using hybrid reinforcement learning and evolutionary algorithms.
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Language
Python
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Last pushed
Jan 19, 2023
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