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.
