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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

Meta-Reinforcement Learning in Data Science

Parth Shukla
Parth Shukla Last Updated : 05 Apr, 2023
5 min read

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

Introduction

Generally, machine learning can be classified into four types: supervised machine learning, unsupervised machine learning, semi-supervised machine learning, and reinforcement learning. Supervised machine learning is a type of machine learning that is the easiest and less complex type or branch of data science. The article will discuss reinforcement learning and meta-reinforcement learning in data science. This article will help one to understand the basic idea and core intuition behind meta-reinforcement learning and its working mechanism. We will start by revisiting the concept of reinforcement learning and quickly move to meta-reinforcement learning and its core intuition.

Reinforcement Learning

Reinforcement learning is a type of machine learning in which three main things are present: the agent, the environment, and the agent’s actions. Here, the agent is the machine learning model or the algorithm that has yet to be initially trained. The agent is put into the environment. Now the agent will perform actions and according to the actions acted and outcomes, the agent, is awarded some points.

Based on points awarded to the model or the agent, the agent learns to stay and act appropriately in the environment, and this is how the model’s training is done in reinforcement learning.

Reinforcement Learning in ML
Source – Google

In the above image, we can see that some input data is being fed to the model. Once the data has been provided to the system, the agent will select the best appropriate model according to the environment, the model will train on the same algorithm and the input data and the basis of outcomes from the same, and some points will award the model. Now, the model can easily select the best-fit algorithm by tuning and looking at the points awarded.

Limited Data Scenario

The time complexity of the reinforcement learning models is high as they require lots of time to train the model. The RL model is the type of model that does lots of computations to introduce a successful model, which also requires higher computational powers. Good reinforcement learning models are trained on significant data sizes to achieve better accuracy and results from the model.

But in every case, it is possible to have lots of data and time to train the reinforcement learning model. In such cases, meta-reinforcement learning helps perform such tasks. The meta-reinforcement knowledge is used in this scenario to prepare the same model faster with limited data available.

Meta-Reinforcement Learning

Meta-reinforcement learning is a type of reinforcement learning used to train reinforcement learning models with limited data and time. This approach is mainly used to train the models where there is no vast data available related to the problem statement, and there is a need to prepare a model as fast as possible.

In this approach, the initial state of the structure of the model is used the most. Here the basic or the fewer stages of the model agent is used to train the agent, and later depending upon this knowledge of the agent, the future steps are performed automatically.

For Example, in the case of neural networks, the basic structure or the initial structure of the neural networks is studied. Now to train the model further, the knowledge gained from the initial steps and the resources available with the same task are used to train and prepare the further model with limited data.

Meta-Reinforcement Learning
Source – Google

In the above image, we can see that the agent acts according to the environment and is awarded by its actions. Here the agent observes the environment and tunes the parameters according to it. The main difference here is the agent takes care of the previous rewards and observations and uses that particular information to perform the next step.

Meta-Reinforcement Learning (Agent & Environment)
Source – Google

In the above Image, we can see an agent and the environment. The agent takes action in the background and is awarded according to its activities. This process takes place multiple times, and the agent takes care of the previous observations and rewards, and the policies are being made to act according to the environment in the next step. This will help the model to perform the task very efficiently with limited data and without taking much time for training.

Reinforcement vs. Meta-Reinforcement Learning

According to the reinforcement model’s structure, both techniques are the same, but there is a slight difference between them in the working mechanism of the model. In reinforcement learning, the model takes actions in the environment and is awarded by the outcomes to form particular activities. Here the data or the observations from the previous steps are not used to perform the following action.

In meta-reinforcement learning, the agent acts according to the environment and takes action. The agent observes the setting for the particular steps and is rewarded according to the outcomes. Noe, in the next step, the agent again acts in the environments, but here, the agent will also remember the observations and rewards from the previous step.

This is the main difference between these two which makes meta-reinforcement learning work faster and more efficiently. The knowledge gained from the previous steps is recorded and helps perform the following steps, which helps to train the model even with limited data.

Conclusion

In this article, we discussed the reinforcement and meta-reinforcement learning techniques with their basic idea, core intuition, and working mechanism. The knowledge about this technique will; help one to understand the concept of the RL algorithms better and allow one to answer the complex interview questions related to it very efficiently.

Some Key Takeaways from this article are:

1. Reinforcement learning is a branch of data science that deals with the agent, environment, and its actions and observations.

2. In regular Reinforcement algorithms, the data or the observations from the previous step are not used to perform the next task.

3. In meta-reinforcement learning, the previous step’s observations and rewards are recorded and included in the next epoch of the agent’s actions.

4. Meta-Reinforcement learning can be beneficial for performing tasks where only a limited amount of data is available, and the study needs to be completed quickly without taking more time.

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Parth Shukla
Parth Shukla

UG (PE) @PDEU | 50+ Published Articles on Data Science | Technical Writer (AI/ML/DL) | Data Science Freelancer | Amazon ML Summer School '22 | Reach Out @[email protected], @portfolio.parthshukla.live

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