BY571/Upside-Down-Reinforcement-Learning
Upside-Down Reinforcement Learning (⅂ꓤ) implementation in PyTorch. Based on the paper published by Jürgen Schmidhuber.
This project offers an implementation of Upside-Down Reinforcement Learning for developing intelligent agents. It takes a desired reward and time horizon as input, then trains an agent to achieve those goals in environments like CartPole or LunarLander. It's designed for researchers and practitioners exploring alternative reinforcement learning paradigms.
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Use this if you are a machine learning researcher or student experimenting with advanced reinforcement learning techniques, specifically Upside-Down RL, for discrete or continuous control problems.
Not ideal if you need a production-ready reinforcement learning solution or are looking for a standard, widely adopted RL algorithm for immediate application.
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MIT
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Aug 13, 2020
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