bprabhakar/upside-down-reinforcement-learning
Pytorch based implementation of Upside Down Reinforcement Learning (UDRL) by J. Schmidhuber et al.
This project helps machine learning researchers and practitioners explore an alternative approach to training AI agents for tasks where actions are rewarded after a sequence of steps. It takes in a simulated environment and produces a trained agent capable of completing specific goals, like landing a spacecraft. This is for anyone researching or implementing advanced AI decision-making systems.
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Use this if you are a reinforcement learning researcher or practitioner interested in experimenting with a novel, supervised learning-based method for agent training on episodic tasks.
Not ideal if you are looking for a plug-and-play solution for common reinforcement learning problems without delving into experimental algorithm implementations.
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Jupyter Notebook
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MIT
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Last pushed
May 01, 2020
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