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RL Assignment 2 - Youtube

Starter Notebook

Use this notebook to get started and make a submission

siddhartha
In [ ]:

What is the notebook about?

Problem - YouTube

This problem deals with a Youtuber, having to employ someone to edit videos

  • Formulate the problem as an MDP
  • Use dynamic programming to find out the optimal policy and optimal values for each month
  • Visualize and explain the results

How to use this notebook? 📝

  • This is a shared template and any edits you make here will not be saved.You should make a copy in your own drive. Click the "File" menu (top-left), then "Save a Copy in Drive". You will be working in your copy however you like.

  • Update the config parameters. You can define the common variables here

Variable Description
AICROWD_DATASET_PATH Path to the file containing test data. This should be an absolute path.
AICROWD_RESULTS_DIR Path to write the output to.
AICROWD_ASSETS_DIR In case your notebook needs additional files (like model weights, etc.,), you can add them to a directory and specify the path to the directory here (please specify relative path). The contents of this directory will be sent to AIcrowd for evaluation.
AICROWD_API_KEY In order to submit your code to AIcrowd, you need to provide your account's API key. This key is available at https://www.aicrowd.com/participants/me

Setup AIcrowd Utilities 🛠

We use this to bundle the files for submission and create a submission on AIcrowd. Do not edit this block.

In [6]:
!pip install -U aicrowd-cli > /dev/null

AIcrowd Runtime Configuration 🧷

Define configuration parameters.

In [7]:
import os

AICROWD_DATASET_PATH = os.getenv("DATASET_PATH", os.getcwd()+"/61c5aa77-62c0-48c9-afef-96d618708b43_data_youtube.zip")
AICROWD_RESULTS_DIR = os.getenv("OUTPUTS_DIR", "results")
API_KEY = "" # Get your key from https://www.aicrowd.com/participants/me (ctrl + click the link)
In [3]:
!aicrowd login --api-key $API_KEY
!aicrowd dataset download -c rl-assignment-2-youtube
API Key valid
Saved API Key successfully!
61c5aa77-62c0-48c9-afef-96d618708b43_data_youtube.zip: 100% 6.35k/6.35k [00:00<00:00, 264kB/s]
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DATASET_DIR = 'data_youtube'
!unzip $AICROWD_DATASET_PATH

Install packages 🗃

Please add all package installations in this section

In [ ]:

Import packages 💻

In [8]:
import numpy as np
import matplotlib.pyplot as plt 
import os
# ADD ANY IMPORTS YOU WANT HERE

Prediction Phase

In [9]:
class YouTuberEnv:

  def __init__(self,kwargs):
    self._verify_params(kwargs)
    self.low_salary = kwargs["low_salary"]
    self.high_salary = kwargs["high_salary"]
    self.low_quit_prob = kwargs["low_quit_prob"]
    self.high_quit_prob = kwargs["high_quit_prob"]
    self.self_edit_cost = kwargs["self_edit_cost"]
    self.low_add_cost = kwargs["low_add_cost"]
    self.high_add_cost = kwargs["high_add_cost"]
    self.low_add_success_prob = kwargs["low_add_success_prob"]
    self.high_add_success_prob = kwargs["high_add_success_prob"]

  def _verify_params(self,kwargs):
    assert "low_salary" in kwargs, "no param for low_salary"
    assert "high_salary" in kwargs, "no param for high_salary"
    assert "low_quit_prob" in kwargs, "no param for low_quit_prob"
    assert "high_quit_prob" in kwargs, "no param for high_quit_prob"
    assert "self_edit_cost" in kwargs, "no param for self_edit_cost"
    assert "low_add_cost" in kwargs, "no param for low_add_cost"
    assert "high_add_cost" in kwargs, "no param for high_add_cost"
    assert "low_add_success_prob" in kwargs, "no param for low_add_success_prob"
    assert "high_add_success_prob" in kwargs, "no param for high_add_success_prob"
In [10]:
def MDP(env):

  states = [0,1] ### DO NOT MODIFY
  actions = []
  rewards = []
  probabilities = []
  extra_info = {}
  ####### INSERT YOUR CODE BELOW. DO NOT EDIT ABOVE THIS LINE ########


  ####### DO NOT EDIT BELOW THIS LINE ########
  mdp = {
          "states":states,
          "actions":actions,
          "rewards":rewards,
          "probabilities":probabilities
         }
  return mdp, extra_info
In [11]:
def DP(mdp):

  states = mdp["states"]
  actions = mdp["actions"]
  rewards = mdp["rewards"]
  probabilties = mdp["probabilities"]

  N = 12 # horizon for 1 year
  n_states = len(states)
  values = np.zeros((N+1, n_states))
  policy = np.random.choice(['L','H'],  size = (N,n_states))
  ### Note: Each value in policy should either be a 'H' or 'L'
  ### Modify the contents of the above 'policy' array
  extra_info = {}
  ####### INSERT YOUR CODE BELOW. DO NOT EDIT ABOVE THIS LINE ########





  ####### DO NOT EDIT BELOW THIS LINE ########
  result = {
            "Values":values,
            "Policy":policy
  }
  return result, extra_info
In [12]:
# DO NOT EDIT THIS CELL
def verify_results(results):
  assert "Values" in results
  assert "Policy" in results
  values = results["Values"]
  policy = results["Policy"]
  N=12
  n_states = 2
  assert np.shape(values) == (N+1,n_states)
  assert np.shape(policy) == (N,n_states)
  unique_values = set(np.unique(policy))
  allowed_values = {'L','H'}
  assert unique_values <= allowed_values

def get_results(kwargs):
  env = YouTuberEnv(kwargs)
  mdp, mdp_info = MDP(env)
  results, dp_info = DP(mdp)
  verify_results(results)
  return results, mdp_info, dp_info
In [13]:
def get_base_params():
  params = {}
  params["low_salary"] = 2300
  params["high_salary"] = 3000
  params["low_quit_prob"] = 0.6
  params["high_quit_prob"] = 0.2
  params["self_edit_cost"] = 4000
  params["low_add_cost"] = 300
  params["high_add_cost"] = 600
  params["low_add_success_prob"] = 0.7
  params["high_add_success_prob"] = 0.9
  return params


base_params = get_base_params()
results, mdp_info, dp_info = get_results(base_params)
print(results)
{'Values': array([[0., 0.],
       [0., 0.],
       [0., 0.],
       [0., 0.],
       [0., 0.],
       [0., 0.],
       [0., 0.],
       [0., 0.],
       [0., 0.],
       [0., 0.],
       [0., 0.],
       [0., 0.],
       [0., 0.]]), 'Policy': array([['L', 'H'],
       ['H', 'L'],
       ['H', 'H'],
       ['H', 'H'],
       ['L', 'L'],
       ['H', 'H'],
       ['L', 'L'],
       ['L', 'H'],
       ['L', 'H'],
       ['H', 'L'],
       ['H', 'H'],
       ['L', 'L']], dtype='<U1')}
In [13]:

In [14]:
if not os.path.exists(AICROWD_RESULTS_DIR):
  os.mkdir(AICROWD_RESULTS_DIR)
if not os.path.exists(DATASET_DIR+'/inputs'):
  os.mkdir(DATASET_DIR+'/inputs')
In [16]:
# Do not edit this cell, generate results with it as is
input_dir = os.path.join(DATASET_DIR, 'inputs')
if not os.path.exists(AICROWD_RESULTS_DIR):
    os.mkdir(AICROWD_RESULTS_DIR)

for params_file in os.listdir(input_dir):
  if ".npy" not in params_file:
    continue
  kwargs = np.load(os.path.join(input_dir, params_file), allow_pickle=True).item()
  results, mdp_info, dp_info = get_results(kwargs)
  idx = params_file.split('_')[-1][:-4]
  np.save(os.path.join(AICROWD_RESULTS_DIR, 'results_' + idx), results)
In [17]:
# Check your score on the given test cases (There are more private test cases not provided)
result_folder = AICROWD_RESULTS_DIR
target_folder = os.path.join(DATASET_DIR, 'targets')

def check_algo_match(results, targets):
    param_results = targets
    param_targets = results

    tv = param_targets['Values'].flatten('F')
    rv_0 = param_results['Values'][:,0]
    rv_1 = param_results['Values'][:,1]
    rewards_match_0 = np.allclose(np.concatenate((rv_0, rv_1)), tv, atol=1e-1)


    rv_0 = param_results['Values'][:,1]
    rv_1 = param_results['Values'][:,0]
    rewards_match_1 = np.allclose(np.concatenate((rv_0, rv_1)), tv, atol=1e-1)


    tp = param_targets['Policy'].flatten('F')
    rp_0 = param_results['Policy'][:,0]
    rp_1 = param_results['Policy'][:,1]
    policy_match_0 = np.concatenate((rp_0, rp_1)) == tp

    rp_0 = param_results['Policy'][:,1]
    rp_1 = param_results['Policy'][:,0]
    policy_match_1 = np.concatenate((rp_0, rp_1)) == tp


    equal = (rewards_match_0 and policy_match_0.all()) or (rewards_match_1 or policy_match_1.all())
    return equal

def check_score(target_folder, result_folder):
    match = []
    for out_file in os.listdir(result_folder):
        res_file = os.path.join(result_folder, out_file)
        results = np.load(res_file, allow_pickle=True).item()
        idx = out_file.split('_')[-1][:-4]  # Extract the file number
        target_file = os.path.join(target_folder, f"targets_{idx}.npy")
        targets = np.load(target_file, allow_pickle=True)[0]
        algo_results = results
        algo_targets = targets
        algo_match = check_algo_match(algo_results, algo_targets)
        match.append(algo_match)
    return np.mean(match)

if os.path.exists(target_folder):
    print("Shared data Score (normalized to 1):", check_score(target_folder, result_folder))
Shared data Score (normalized to 1): 0.0

Answer the following

Consider a policy where you always pay the employee low income and allocate a high advertising budget. Is it optimal? Justify your answer. (Based on the data provided in the assignment question)

Your answer:

Submit to AIcrowd 🚀

In [ ]:
!DATASET_PATH=$AICROWD_DATASET_PATH \
aicrowd notebook submit \
    -c rl-assignment-2-youtube -a assets
WARNING: Assets directory is empty
/usr/local/lib/python3.7/dist-packages/aicrowd/notebook/helpers.py:361: UserWarning: `%aicrowd` magic command can be used to save the notebook inside jupyter notebook/jupyterLab environment and also to get the notebook directly from the frontend without mounting the drive in colab environment. You can use magic command to skip mounting the drive and submit using the code below:
 %load_ext aicrowd.magic
%aicrowd notebook submit -c rl-assignment-2-youtube -a assets
  warnings.warn(description + code)
Using notebook: Problem 1 YouTube starter notebook for submission...
Removing existing files from submission directory...
Scrubbing API keys from the notebook...
Collecting notebook...
Validating the submission...
Executing install.ipynb...
[NbConvertApp] Converting notebook /content/submission/install.ipynb to notebook
[NbConvertApp] Executing notebook with kernel: python3
[NbConvertApp] Writing 1034 bytes to /content/submission/install.nbconvert.ipynb
Executing predict.ipynb...
[NbConvertApp] Converting notebook /content/submission/predict.ipynb to notebook
[NbConvertApp] Executing notebook with kernel: python3
[NbConvertApp] ERROR | unhandled iopub msg: colab_request
[NbConvertApp] ERROR | unhandled iopub msg: colab_request
[NbConvertApp] ERROR | unhandled iopub msg: colab_request
[NbConvertApp] ERROR | unhandled iopub msg: colab_request
[NbConvertApp] ERROR | unhandled iopub msg: colab_request
[NbConvertApp] ERROR | unhandled iopub msg: colab_request
[NbConvertApp] Writing 21903 bytes to /content/submission/predict.nbconvert.ipynb
submission.zip โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 100.0% โ€ข 18.8/17.1 KB โ€ข 1.6 MB/s โ€ข 0:00:00
                                                           โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ                                                            
                                                           โ”‚ Successfully submitted! โ”‚                                                            
                                                           โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ                                                            
                                                                 Important links                                                                  
โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚  This submission โ”‚ https://www.aicrowd.com/challenges/iit-m-2021-assignment-2/problems/rl-assignment-2-youtube/submissions/158466              โ”‚
โ”‚                  โ”‚                                                                                                                             โ”‚
โ”‚  All submissions โ”‚ https://www.aicrowd.com/challenges/iit-m-2021-assignment-2/problems/rl-assignment-2-youtube/submissions?my_submissions=true โ”‚
โ”‚                  โ”‚                                                                                                                             โ”‚
โ”‚      Leaderboard โ”‚ https://www.aicrowd.com/challenges/iit-m-2021-assignment-2/problems/rl-assignment-2-youtube/leaderboards                    โ”‚
โ”‚                  โ”‚                                                                                                                             โ”‚
โ”‚ Discussion forum โ”‚ https://discourse.aicrowd.com/c/iit-m-2021-assignment-2                                                                     โ”‚
โ”‚                  โ”‚                                                                                                                             โ”‚
โ”‚   Challenge page โ”‚ https://www.aicrowd.com/challenges/iit-m-2021-assignment-2/problems/rl-assignment-2-youtube                                 โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
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