WorldModels

An implementation of the ideas from this paper https://arxiv.org/pdf/1803.10122.pdf

Code base adapted from https://github.com/hardmaru/estool

This repo. was created with an objective to learn Generative modeling in an enclosed Open-AI's environment with Evolutionary Strategy using the CMA-ES algorithm. There are some ongoing experiments for the next steps with different backbones and Attention units.

For full installation and run instructions:

• Create the anaconda environment file from requirement.txt
• Base frameworks in use are Keras-GPU(v2.3.1) and Tensorflow-GPU(v2.1)
• Run the notebooks for analysis of the experiments based on the weights and collected data
• For rendering the benchmarks, in OpenAI Gym environment:

python model.py car_racing --filename ./controller/car_racing.cma.xxxxx.best.json --render --final_mode

• The videos for some agents reaching some good scores are here
• The complete instructions for rendering and benchmark will be updated soon...

Architecture for the VAE (Backbone)

Architecture for the RNN

Box/Machine configuration: Experimentations done on a AMD's 8-core CPU with a Nvidia's GeForce RTX-2070 (Turing)