Reinforcement Learning Algorithms with Python

Creating a DAgger loop

It's now time to set up the core of the DAgger algorithm. The outline has already been defined in the pseudocode in The DAgger algorithm section, but let's take a more in-depth look at how it works:

1. Initialize the dataset composed of two lists, X and y, where we'll put the states visited and the expert target actions. We also initialize the environment:

    X = []    y = []    env = FlappyBird()    env = PLE(env, fps=30, display_screen=False)    env.init() 

2. Iterate across all the DAgger iterations. At the beginning of every DAgger iteration, we have to reinitialize the learner computational graph (because we retrain the learner on every iteration on the new dataset), reset the environment, and run a number of random actions. At ...