Deep Reinforcement Learning Algorithms

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.

Name		Name	Last commit message	Last commit date
Latest commit History 17 Commits
Policy_Gradient_Methods_Theory.ipynb		Policy_Gradient_Methods_Theory.ipynb
README.md		README.md
distributional_rl.ipynb		distributional_rl.ipynb
dqn.ipynb		dqn.ipynb
dqn_replay_buffer.ipynb		dqn_replay_buffer.ipynb
implicit-quantile-networks-theory.ipynb		implicit-quantile-networks-theory.ipynb
unifying_count_based_exploration_intrinsic_motivation.ipynb		unifying_count_based_exploration_intrinsic_motivation.ipynb
variational-autoencoders.ipynb		variational-autoencoders.ipynb

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