vaibhawvipul

CREATE MODEL `velotio_tutorial.sample_model`
OPTIONS(model_type='logistic_reg') AS
SELECT
  IF(totals.transactions IS NULL, 0, 1) AS label,
  IFNULL(device.operatingSystem, "") AS os,
  device.isMobile AS is_mobile,
  IFNULL(geoNetwork.country, "") AS country,
  IFNULL(totals.pageviews, 0) AS pageviews
FROM
  `bigquery-public-data.google_analytics_sample.ga_sessions_*`

vaibhawvipul

WITH question_answers_join AS (
  SELECT *
    , GREATEST(1, TIMESTAMP_DIFF(answers.first, creation_date, minute)) minutes_2_answer
  FROM (
    SELECT id, creation_date, title
      , (SELECT AS STRUCT MIN(creation_date) first, COUNT(*) c
         FROM `bigquery-public-data.stackoverflow.posts_answers` 
         WHERE a.id=parent_id
      ) answers
      , SPLIT(tags, '|') tags

vaibhawvipul

import csv
import random
import math

def loadDataset(filename, split):
        training_data = []
        test_data = []
        with open(filename, 'rb') as csvfile:
            lines = csv.reader(csvfile)
            dataset = list(lines)

vaibhawvipul

Synopsis

This is a Deep Reinforcement Learning solution to some classic control problems. I've used it to solve MountainCar-v0 problem, CartPole-v0 and [CartPole-v1] (https://gym.openai.com/envs/CartPole-v1) in OpenAI's Gym. This code uses Tensorflow to model a value function for a Reinforcement Learning agent. I've run it with Tensorflow 1.0 on Python 3.5 under Windows 7.

Some of the hyperparameters used in the main.py script to solve MountainCar-v0 have been optained partly through exhaustive search, and partly via Bayesian optimization with Scikit-Optimize. The optimized hyperparameters and their values are:

• Size of 1st fully connected layer: 198
• Size of 2nd fully connected layer: 96
• Learning rate: 2.33E-4
• Period (in steps) for the update of the target network parameters as per the DQN algorithm: 999