Python Reinforcement Learning Projects

controller.py

The controller.py module is where everything comes together. We will implement the Controller, which handles training each child network as well as its own parameter updates. We first implement a helper function that calculates an exponential moving average of a list of numbers. We use this as the baseline function for our REINFORCE gradient calculation, as mentioned previously, to calculate the exponential moving average of the past rewards:

import loggingimport numpy as npimport tensorflow as tffrom child_network import ChildCNNfrom cifar10_processor import get_tf_datasets_from_numpyfrom config import child_network_params, controller_paramslogger = logging.getLogger(__name__)def ema(values):    """ Helper function for keeping ...