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""" 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

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# -*- coding: utf-8 -*-
"""
Created on Mon Sep 23 23:16:44 2017

@author: Marios Michailidis

This is an example that performs stacking to improve mean squared error
This examples uses 2 bases learners (a linear regression and a random forest)
and  linear regression (again) as a meta learner to achieve the best score.
The initial train data are split in 2 halves to commence the stacking. 

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import time
import socket
import math
import json

addr = ("127.0.0.1", 12000)

clientSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
clientSocket.settimeout(1)

ucaiado

Setting up github and bitbucket on the same computer

Github will be the main account and bitbucket the secondary.

Create SSH Keys

ssh-keygen -t rsa -C "github email"

Enter passphrase when prompted. If you see an option to save the passphrase in your keychain, do it for an easier life.

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