A series of notebooks containing theory and implementions for a suite of Deep RL Algorithms.
These are meant to serve as:
- A refresher exercise for myself on Deep RL and Tensorflow (especially TF2)
- A set of reference implementations of some basic RL components that I need for my own RL research. Once these are working well I'm going to try out some new exploration algorithms that I've been developing.
Libraries and tools used:
- All of these are Google Colab Notebooks: https://colab.research.google.com/notebooks/intro.ipynb
- All of them are written in Tensorflow (mostly TF2, but some are in TF1)
- Some use the Sonnet library of neural network models, some use Keras.