This project collects a set of neuroevolution experiments with/towards deep networks for reinforcement learning using an unsupervised learning feature exctactor.
First make sure the OpenAI Gym is pip-installed on python3, instructions here.
You will also need the GVGAI_GYM to access GVGAI environments.
Clone this repository, then execute:
$ bundle install
bundle exec ruby experiments/cartpole.rb
Bug reports and pull requests are welcome on GitHub at https://github.com/giuse/DNE.
The gem is available as open source under the terms of the MIT License.
Please feel free to contribute to this list (see Contributing
above).
- UL-ELR stands for Unsupervised Learning plus Evolutionary Reinforcement Learning, from the paper "Intrinsically Motivated Neuroevolution for Vision-Based Reinforcement Learning" (ICDL2011). Check here for citation reference and pdf.
- BD-NES stands for Block Diagonal Natural Evolution Strategy, from the homonymous paper "Block Diagonal Natural Evolution Strategies" (PPSN2012). Check here for citation reference and pdf.
- RNES stands for Radial Natural Evolution Strategy, from the paper "Novelty-Based Restarts for Evolution Strategies" (CEC2011). Check [here](https://exascale.info/members/
- Online VQ stands for Online Vector Quantization, from the paper "Intrinsically Motivated Neuroevolution for Vision-Based Reinforcement Learning" (ICDL2011). Check here for citation reference and pdf.
- The OpenAI Gym is described here and available on this repo
- PyCall.rb is available on this repo.