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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

REINFORCE Algorithm: Taking baby steps in reinforcement learning

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Guest Blog Last Updated : 24 Nov, 2020
5 min read

Introduction

Lets’ solve OpenAI’s Cartpole, Lunar Lander, and Pong environments with REINFORCE algorithm.

Reinforcement learning is arguably the coolest branch of artificial intelligence. It has already proven its prowess: stunning the world, beating the world champions in games of Chess, Go, and even DotA 2.

In this article, I would be walking through a fairly rudimentary algorithm, and showing how even this can achieve a superhuman level of performance in certain games.

Reinforcement Learning deals with designing “Agents” that interacts with an “Environment” and learns by itself how to “solve” the environment by systematic trial and error. An environment could be a game like chess or racing, or it could even be a task like solving a maze or achieving an objective. The agent is the bot that performs the activity.

Reinforcement Learning - Reinforce

An agent receives “rewards” by interacting with the environment. The agent learns to perform the “actions” required to maximize the reward it receives from the environment. An environment is considered solved if the agent accumulates some predefined reward threshold. This nerd talk is how we teach bots to play superhuman chess or bipedal androids to walk.

REINFORCE Algorithm

REINFORCE belongs to a special class of Reinforcement Learning algorithms called Policy Gradient algorithms. A simple implementation of this algorithm would involve creating a Policy: a model that takes a state as input and generates the probability of taking an action as output. A policy is essentially a guide or cheat-sheet for the agent telling it what action to take at each state. The policy is then iterated on and tweaked slightly at each step until we get a policy that solves the environment.

The policy is usually a Neural Network that takes the state as input and generates a probability distribution across action space as output.

Policy: Example

The objective of the policy is to maximize the “Expected reward”.

Each policy generates the probability of taking an action in each station of the environment.

Policy 1 vs Policy 2 — Different Trajectories

The agent samples from these probabilities and selects an action to perform in the environment. At the end of an episode, we know the total rewards the agent can get if it follows that policy. We backpropagate the reward through the path the agent took to estimate the “Expected reward” at each state for a given policy.

Reinforce - Expected reward

Here the discounted reward is the sum of all the rewards the agent receives in that future discounted by a factor Gamma.

Discounted Reward

The discounted reward at any stage is the reward it receives at the next step + a discounted sum of all rewards the agent receives in the future.

The discounted factor is calculated for each state by back-propagating the rewards.

For the above equation this is how we calculate the Expected Reward:

Expected Reward

As per the original implementation of the REINFORCE algorithm, the Expected reward is the sum of products of a log of probabilities and discounted rewards.

Algorithm steps

The steps involved in the implementation of REINFORCE would be as follows:

  1. Initialize a Random Policy (a NN that takes the state as input and returns the probability of actions)
  2. Use the policy to play N steps of the game — record action probabilities-from policy, reward-from environment, action — sampled by agent
  3. Calculate the discounted reward for each step by backpropagation
  4. Calculate expected reward G
  5. Adjust weights of Policy (back-propagate error in NN) to increase G
  6. Repeat from 2

Check out the implementation using Pytorch on my Github.

Demos

I have tested out the algorithm on Pong, CartPole, and Lunar Lander. It takes forever to train on Pong and Lunar Lander — over 96 hours of training each on a cloud GPU. There are several updates on this algorithm that can make it converge faster, which I haven’t discussed or implemented here. Checkout Actor-Critic models and Proximal Policy Optimization if interested in learning further.

CartPole

Image for post

State:

Horizontal Position, Horizontal Velocity, Angle of the pole, Angular Velocity

Actions:

Push cart left, Push cart right

Random Policy Play:

Random Play- Total Reward 18

Random Play- Total Reward 18

Agent Policy Trained with REINFORCE:

Trained Agent- Total Reward 434

Trained Agent- Total Reward 434

Lunar Lander

Reinforce Lunar Lander

Random Play Agent

State:

The state is an array of 8 vectors. I am not sure what they represent.

Actions:

0: Do nothing

1: Fire Left Engine

2: Fire Down Engine

3: Fire Right Engine

Agent Policy Trained with REINFORCE:

Lunar Lander trained with REINFORCE

Lunar Lander trained with REINFORCE

Pong

This was much harder to train. Trained on a GPU cloud server for days.

State: Image

Actions: Move Paddle Left, Move Paddle Right

Trained Agent

Trained agent

Reinforcement Learning has progressed leaps and bounds beyond REINFORCE. My goal in this article was to 1. learn the basics of reinforcement learning and 2. show how powerful even such simple methods can be in solving complex problems. I would love to try these on some money-making “games” like stock trading … guess that’s the holy grail among data scientists.

Github Repo: https://github.com/kvsnoufal/reinforce

Shoulders of giants:

  1. Policy Gradient Algorithms (https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html)
  2. Deriving REINFORCE (https://medium.com/@thechrisyoon/deriving-policy-gradients-and-implementing-reinforce-f887949bd63)
  3. Udacity’s reinforcement learning course (https://github.com/udacity/deep-reinforcement-learning)

About The Author

Author

Noufal kvs

I work in Dubai Holding, UAE as a data scientist. You can reach out to me at [email protected] or https://www.linkedin.com/in/kvsnoufal/

reinforce algorithm
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Snehal Raj
Snehal Raj

When explaining the algorithms, please use - backtrack the rewards instead of -backpropagate the rewards

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Simple Beginner’s guide to Reinforcement Learning & its implementation
Simple Beginner’s guide to Reinforcement Learning & its implementation
What is Reinforcement Learning and How Does It Work (Updated 2024)
What is Reinforcement Learning and How Does It Work (Updated 2024)

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