I've been playing with reinforcement learning on simulated robots, using OpenAI Gym[0] and PyBullet[1]. I find it interesting because, in theory, the learned p

I've been playing with reinforcement learning on simulated robots, using OpenAI Gym[0] and PyBullet[1]. I find it interesting because, in theory, the learned policies transfer to real-world robots, and the time is nigh for development in that area. Plus you get to watch it do funky robot shit and slowly get better. If you want a quick way to dive in, I have a repo[2] I've been using to train on a variety of PCs and Google Colab[3]. The task is a Panda robot pushing a randomly placed object. I had some trouble getting it all working with recent Keras at first, so my repo might save you time. Email me[4] if you get stuck. [0] https://gym.openai.com/ https://gym.openai.com/ [1] https://pybullet.org/ https://pybullet.org/ [2] https://github.com/tlack/rl-experiments https://github.com/tlack/rl-experiments [3] https://colab.research.google.com/drive/1HGmKMGwW_emok157ENr-PQjvSuKmSMzx?usp=sharing https://colab.research.google.com/drive/1HGmKMGwW_emok157ENr... [4] lackner@gmail.com