Upside-Down-Reinforcement-Learning

Upside-Down Reinforcement Learning (⅂ꓤ) implementation in Pytorch.
Based on the paper published by Jürgen Schmidhuber: ⅂ꓤ-Paper

This repository contains a discrete action space as well as a continuous action space implementation for the OpenAI gym CartPole environment (continuous version of the environment).

The notebooks include the training of a behavior function as well as an evaluation part, where you can test the trained behavior function. Feed it with an desired reward that the agent shall achieve in a desired time horizon.

Plots for the discrete CartPole Environment:

Plots for the continuous CartPole Environment:

Plots for the LunarLander Environment:

TODO:

• test some possible improvements mentioned in the paper (6. Future Research Directions).

Author

• Sebastian Dittert

Feel free to use this code for your own projects or research. For citation check DOI or cite as:

@misc{Upside-Down,
  author = {Dittert, Sebastian},
  title = {PyTorch Implementation of Upside-Down RL},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/BY571/Upside-Down-Reinforcement-Learning}},
}