The following directions explain how to setup your Jupyter development environment. The goal is to isolate each one of your projects in their own "virtual environment" (a.k.a. conda environment), so you can easily switch between projects without contaminating your installations, dependencies, etc.
Table of Contents
| from IPython.display import HTML | |
| from IPython.display import display | |
| # Taken from https://stackoverflow.com/questions/31517194/how-to-hide-one-specific-cell-input-or-output-in-ipython-notebook | |
| tag = HTML('''<script> | |
| code_show=true; | |
| function code_toggle() { | |
| if (code_show){ | |
| $('div.cell.code_cell.rendered.selected div.input').hide(); | |
| } else { |
| """ | |
| A bare bones examples of optimizing a black-box function (f) using | |
| Natural Evolution Strategies (NES), where the parameter distribution is a | |
| gaussian of fixed standard deviation. | |
| """ | |
| import numpy as np | |
| np.random.seed(0) | |
| # the function we want to optimize |
| """ Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
| import numpy as np | |
| import cPickle as pickle | |
| import gym | |
| # hyperparameters | |
| H = 200 # number of hidden layer neurons | |
| batch_size = 10 # every how many episodes to do a param update? | |
| learning_rate = 1e-4 | |
| gamma = 0.99 # discount factor for reward |