September 14, 2019

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inoryy/tensorflow2-deep-reinforcement-learning

inoryy/tensorflow2-deep-reinforcement-learning

Code accompanying the blog post “Deep Reinforcement Learning with TensorFlow 2.1”

repo name inoryy/tensorflow2-deep-reinforcement-learning
repo link https://github.com/inoryy/tensorflow2-deep-reinforcement-learning
homepage http://inoryy.com/post/tensorflow2-deep-reinforcement-learning/
language Jupyter Notebook
size (curr.) 96 kB
stars (curr.) 168
created 2019-01-19
license

Deep Reinforcement Learning with TensorFlow 2.1

Source code accompanying the blog post Deep Reinforcement Learning with TensorFlow 2.1.

In the blog post, I showcase the TensorFlow 2.1 features through the lens of deep reinforcement learning by implementing an advantage actor-critic agent, solving the classic CartPole-v0 environment. While the goal is to showcase TensorFlow 2.1, I also provide a brief overview of the DRL methods.

You can view the code either as a notebook, a self-contained script, or execute it online with Google Colab.

To run it locally, install the dependencies with pip install -r requirements.txt, and then execute python a2c.py.

To control various hyperparameters, specify them as flags, e.g. python a2c.py --batch_size=256.

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