Upside-Down-Reinforcement-Learning and upside-down-reinforcement-learning
These are competitors, as both repositories provide PyTorch implementations of the Upside-Down Reinforcement Learning algorithm by J. Schmidhuber, offering alternative choices for the same functionality.
About Upside-Down-Reinforcement-Learning
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.
About upside-down-reinforcement-learning
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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