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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

Getting to Grips with Reinforcement Learning via Markov Decision Process

Sreenath
Sreenath Last Updated : 01 Dec, 2020
3 min read

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

Introduction

Reinforcement Learning (RL) is a learning methodology by which the learner learns to behave in an interactive environment using its own actions and rewards for its actions. The learner, often called, agent, discovers which actions give the maximum reward by exploiting and exploring them.

Reinforce algorithm

A key question is – how is RL different from supervised and unsupervised learning?

The difference comes in the interaction perspective. Supervised learning tells the user/agent directly what action he has to perform to maximize the reward using a training dataset of labeled examples. On the other hand, RL directly enables the agent to make use of rewards (positive and negative) it gets to select its action. It is thus different from unsupervised learning as well because unsupervised learning is all about finding structure hidden in collections of unlabelled data.

 

Reinforcement Learning Formulation via Markov Decision Process (MDP)

The basic elements of a reinforcement learning problem are:

  • Environment: The outside world with which the agent interacts
  • State: Current situation of the agent
  • Reward: Numerical feedback signal from the environment
  • Policy: Method to map the agent’s state to actions. A policy is used to select an action at a given state
  • Value: Future reward (delayed reward) that an agent would receive by taking an action in a given state

Markov Decision Process (MDP) is a mathematical framework to describe an environment in reinforcement learning. The following figure shows agent-environment interaction in MDP:

reinforcement learning markov

More specifically, the agent and the environment interact at each discrete time step, t = 0, 1, 2, 3…At each time step, the agent gets information about the environment state St. Based on the environment state at instant t, the agent chooses an action At. In the following instant, the agent also receives a numerical reward signal Rt+1. This thus gives rise to a sequence like S0, A0, R1, S1, A1, R2…

The random variables Rt and St have well defined discrete probability distributions. These probability distributions are dependent only on the preceding state and action by virtue of Markov Property. Let S, A, and R be the sets of states, actions, and rewards. Then the probability that the values of St, Rt and At taking values s’, r and a with previous state s is given by,

reinforcement learning markov - formula

The function p controls the dynamics of the process.

Let’s Understand this Using an Example

Let us now discuss a simple example where RL can be used to implement a control strategy for a heating process.

The idea is to control the temperature of a room within the specified temperature limits. The temperature inside the room is influenced by external factors such as outside temperature, the internal heat generated, etc.

The agent, in this case, is the heating coil which has to decide the amount of heat required to control the temperature inside the room by interacting with the environment and ensure that the temperature inside the room is within the specified range. The reward, in this case, is basically the cost paid for deviating from the optimal temperature limits.

The action for the agent is the dynamic load. This dynamic load is then fed to the room simulator which is basically a heat transfer model that calculates the temperature based on the dynamic load. So, in this case, the environment is the simulation model. The state variable St contains the present as well as future rewards.

The following block diagram explains how MDP can be used for controlling the temperature inside a room:

example

Limitations of this Method

Reinforcement learning learns from the state. The state is the input for policymaking. Hence, the state inputs should be correctly given. Also as we have seen, there are multiple variables and the dimensionality is huge. So using it for real physical systems would be difficult!

 

Further Reading

To know more about RL, the following materials might be helpful:

blogathonMarkovMDPReinforcement Learning
Sreenath
Sreenath

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