/tensorflow2-deep-reinforcement-learning

Code accompanying the blog post "Deep Reinforcement Learning with TensorFlow 2.1"

Primary LanguageJupyter Notebook

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.