p0bailey

1. A Comprehensive Guide to Terraform.

2. Why we use Terraform and not Chef, Puppet, Ansible, SaltStack, or CloudFormation.

3. An Introduction to Terraform.

4. How to manage Terraform state

5. How to create reusable infrastructure with Terraform modules

kashif

Cross Entropy Method

How do we solve for the policy optimization problem which is to maximize the total reward given some parametrized policy?

Discounted future reward

To begin with, for an episode the total reward is the sum of all the rewards. If our environment is stochastic, we can never be sure if we will get the same rewards the next time we perform the same actions. Thus the more we go into the future the more the total future reward may diverge. So for that reason it is common to use the discounted future reward where the parameter discount is called the discount factor and is between 0 and 1.

A good strategy for an agent would be to always choose an action that maximizes the (discounted) future reward. In other words we want to maximize the expected reward per episode.

alvations

from nltk.corpus import wordnet as wn
from nltk.stem import PorterStemmer, WordNetLemmatizer
#from nltk import pos_tag, word_tokenize

# Pywsd's Lemmatizer.
porter = PorterStemmer()
wnl = WordNetLemmatizer()

from nltk.tag import PerceptronTagger

PurpleBooth

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites