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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

Reinforcement Learning and its Scope in 2025

Suvrat
Suvrat Last Updated : 13 Dec, 2024
4 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Artificial Intelligence, Machine Learning and Data Science have been ruling the tech buzzword dictionary for the past couple few years. Whether movies depicting the threat of an algorithmic takeover or self-driving cars gradually taking over roads – Machine Learning has seeped into every sphere. When we speak of self-driving cars and machines simulating the human mind – it can all be made possible with Reinforcement Learning. In this article, we will explore what RL is, its applications, its challenges and the future it awaits.

Reinforcement Learning

What is Reinforcement Learning (RL)?

Reinforcement Learning (RL) is a subdomain of Machine Learning wherein an agent learns by interacting with the environment. While in other Machine/Deep Learning techniques, our models learn solely on the grounds of the data fed to them, RL makes it possible for our model (usually referred to as ‘agent’) to learn continually from experience. If the agent performs a desirable action, it is rewarded; likewise, for an undesirable one, it is penalized. This classifies our reinforcements into two categories-

  1. Positive Reinforcement: Whenever our model performs an advantageous action, positive behaviour is added to the model, making the action more likely to be repeated.
  2. Negative Reinforcement: Whenever our model performs an uncalled for action, positive behaviour is added to the model, making the action less likely to be repeated.
Reinforcement Learning

Reinforcement Learning

Thus, simply put, Reinforcement Learning closely mimics human learning patterns – observation, trial and error.

For example, let us consider a game of chess. Our agent begins to play the game with an absolute trial and error approach. Every time it wins, it is rewarded, and when it loses, it is accordingly penalized. Gradually, the agent learns how to play and win a chess game. This is of the most classic exemplifications of Reinforcement Learning.

We can thus conclude that RL is a way that enables AI systems to learn on their own by perceiving the environment without having to feed them with a massive amount of labelled data.

Applications of Reinforcement Learning

Now that we are familiar with the concept of Reinforcement Learning, let’s look into some of its applications:

  1. Autonomous/Self-Driving Cars
  2. Automatic Traffic Light Control System
  3. Robotic Surgeons
  4. NLP Applications such as text summarization, question-answering systems, etc.
  5. Customized Recommendation Systems
Applications of Reinforcement Learning
Self-Driving Cars – A use case of Reinforcement Learning (Souce: Creative Commons)

Challenges in Reinforcement Learning

Although Reinforcement Learning has appeared as a new touchstone in the Machine Learning arena and has been the centre of attention for researchers, it happens to face specific challenges that are enlisted below:

1. Large Datasets: Since Reinforcement Learning Models are complex, they need massive datasets to make better decisions.

2. Environment Dependency: As we know that the Reinforcement Learning Models learn based on the agent’s interactions with the environment – it causes hindrance in the training of the model; the agent learns based on the current state of the environment, and for a constantly changing environment, it becomes difficult for the agent to get trained.

3. Design of Reward Structure: For any real worlds use case of RL, one needs to analyze the problem statement and devise an appropriate structure as to when the model should be awarded and when should it be penalized. This remains another problem that the researchers are constantly in the face of.

Scope of RL

Given its extensive applications, one can quite presciently say that Reinforcement Learning stares into a bright future. Unlike other Machine Learning methods, RL does not require labelled datasets and makes real-life decisions based on a reward system – mimicking the human behaviour to the closest. It serves as an impeccable resolve for situations when the target our problem statement is trying to accomplish is clear, but the way of getting there is not. Though, as of now, in 2022, the real-world applications of RL are limited and are not in constant circulation in our daily lives, With the tenacious and committed researchers constantly digging deep into the field of RL, we’ll surely break through all the challenges and resistance the current studies face and revolutionize the Artificial Intelligence sphere.

Conclusion

This article was an introduction to the concepts of reinforcement learning. Let us quickly recap the key takeaways:

– RL involves an agent that interacts with the external environment and learns with every action.

– For every favourable action, the agent is rewarded positively; correspondingly, for every unfavourable one, the agent is penalized.

– There are a multitude of RL-based applications ranging from Self-Driving Cars to automatic recommendation systems.

– Lack of large datasets, environment dependency of the agent’s learning and appropriate design of the reward structure for the agent – are some of the most considerable challenges in Reinforcement Learning.

However, despite all challenges, we can see gradual improvements which project the fact that the future for Reinforcement Learning is a bright one.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Suvrat
Suvrat

I'm Suvrat Arora, a Computer Science graduate. Enthusiastic about AI, Data Science, ML and NLP - I believe that storytelling is a significant aspect of life which has led me to develop a practice of documenting, organizing, and disseminating knowledge across domains, making me an active contributor on multiple platforms.

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