This repository contains codes that I have reproduced (while learning RL) for various reinforcement learning algorithms. The codes were tested on Colab.

If Github is not loading the Jupyter notebooks, a known Github issue, click here to view the notebooks on Jupyter's nbviewer.

^{(1): N-step returns used for critic's target.
(2): GAE used for computation of TD lambda return (for critic's target) & policy's advantage.
(3): Distributed Tensorflow & Python's multiprocessing package used.
(4): State featurization (approximates feature map of an RBF kernel) is used.
(5): Fast-slow LSTM with an overly simplified VAE like "variational unit" (VU) is used.}

The misc folder contains related example codes that I have put together while learning RL. See the README.md in the misc folder for more details.

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