Deep reinforcement learning using an asynchronous advantage actor-critic (A3C) model written in TensorFlow.
This AI does not rely on hand-engineered rules or features. Instead, it masters the environment by looking at raw pixels and learning from experience, just as humans do.
For Pong, an average score of 18 was reached in 72 hours of training on an 8-core CPU. Training and evaluation code is available at github.com/andreimuntean/a3c.
Deep reinforcement learning for environments with small state spaces.
The CartPole Gym environment was mastered in just a few minutes of training. Training and evaluation code is available at github.com/andreimuntean/deep-q-learning-lite.
Deep reinforcement learning using an asynchronous advantage actor-critic (A3C) model written in TensorFlow.
This AI does not rely on hand-engineered rules or features. Instead, it masters the environment by looking at raw pixels and learning from experience, just as humans do.
The Flappy Bird Gym environment was mastered in 48 hours of training using a CPU with 8 cores. Training and evaluation code is available at github.com/andreimuntean/a3c.
Here are instructions to set up TensorFlow dev environment on Docker if you are running Windows, and configure it so that you can access Jupyter Notebook from within the VM + edit files in your text editor of choice on your Windows machine.
First, install https://www.docker.com/docker-toolbox
Since this is Windows, creating the Docker group "docker" is not necessary.
