REINFORCEMENT LEARNING WITH R ALGORITHMS AGENTS ENVIRONMENT

Algorithms and Data Structures Online Training

Working Professionals and Freshers

Online and Classroom Sessions

Week Days and Week Ends

2 Months

•Basic to Advance concepts of Algorithms and Data Structures

•How to create delete and view Algorithms and Data Structures.

•Learn to code with Algorithms and Data Structures the easy way.

•How to create your own Algorithms and Data Structures components from scratch.

•Learning and Creating a complete Algorithms and Data Structures project in depth

•Learn all important topics of Algorithms and Data Structures by practical examples

•This course will teach you how to get moving in Algorithms and Data Structures.

•Learn Algorithms and Data Structures. Become Developer in Test and Kick-start your Career in IT.

•You will be able to develop top class apps and think like a programmer

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•Career guidance providing by It Expert

•25+ projects for good Learning experience

•Highly competent and skilled IT instructors

•Online Training with 100% placement assistance

•We provide Classroom and Online training in Metro Cities

•Repeating of lectures allowed (based on seat availability)

•Make aware of code competence in building extensive range of applications using Python

•Lifetime access to our 24×7 online support team who will resolve all your technical queries, through ticket based tracking system.

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•Asp.net Mvc Developer, Asp.net Mvc Lead, Java Developer(spring), Ui Developer, Ui Lead, Data Architect

•IOS Developer, .net c# asp.net, c c++ java, accounts finance sap fico, sap mm functional consultant

•Java Fullstack Developer, Java, Javascript, Data Structures, OOPS, Cassandra, NoSQL, Big Data, CI, XSLT, Maven, XML, Web Services, Microservices, SQL, Rest

•QA and Testing, erp, IMS, Cloud Computing, asp.net vb.net c# c c++, core java j2ee, oracle plsql unix shell script, cobol jcl db2 vsam cics, Sharepoint C#

•SQL Developer, Oracle Developer, Tibco, Datawarehousing, Automation Testing, Websphere Admin, Weblogic Support, Python, Netezza, Actuate, Hadoop

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•Reinforcement Learning Techniques with R

•The Course Overview

•Understanding the RL Grid World Problem

•Implementing the Grid World Framework in R

•Navigating Grid World and Calculating Likely Successful Outcomes

•R Example Finding Optimal Policy Navigating x Grid

•R Example Updating Optimal Policy Navigating x Grid

•R Example MDPtoolbox Solution Navigating x Grid

•More MDPtoolbox Function Examples Using R

•R Example Finding Optimal x Grid World Policy

•R Exercise Building a x Grid World Environment

•R Exercise Solution Building a x Grid World Environment

•Practical Reinforcement Learning Agents and Environments

•Install RStudio

•Install Python

•Launch Jupyter Notebook

•Learning Type Distinctions

•Get Started with Reinforcement Learning

•Realworld Reinforcement Learning Examples

•Key Terms in Reinforcement Learning

•OpenAI Gym

•Monte Carlo Method

•Monte Carlo Method in Python

•Monte Carlo Method in R

•Practical Reinforcement Learning in OpenAI Gym

•Markov Decision Process Concepts

•Python MDP Toolbox

•Value and Policy Iteration in Python

•MDP Toolbox in R

•Value Iteration and Policy Iteration in R

•Temporal Difference Learning

•Temporal Difference Learning in Python

•Temporal Difference Learning in R

•Discover Algorithms for RewardBased Learning in R

•R Example Building ModelFree Environment

•R Example Finding ModelFree Policy

•R Example Finding ModelFree Policy Continued

•R Example Validating ModelFree Policy

•Policy Evaluation and Iteration

•R Example Moving a Pawn with Changed Parameters

•Discount Factor and Policy Improvement

•Monte Carlo Methods

•Environment and QLearning Functions with R

•Learning Episode and StateAction Functions in R

•StateActionRewardStateAction SARSA

•Simulated Annealing An Alternative to QLearning

•QLearning with a Discount Factor

•Visual QLearning Examples

•Understanding the RL “Grid World” Problem

•R Example – Finding Optimal Policy Navigating 2 x 2 Grid

•R Example – Updating Optimal Policy Navigating 2 x 2 Grid

•R Example – MDPtoolbox Solution Navigating 2 x 2 Grid

•R Example – Finding Optimal 3 x 4 Grid World Policy

•R Exercise – Building a 3 x 4 Grid World Environment

•R Exercise Solution – Building a 3 x 4 Grid World Environment

•Practical Reinforcement Learning – Agents and Environments

•Real-world Reinforcement Learning Examples

•Discover Algorithms for Reward-Based Learning in R

•R Example – Building Model-Free Environment

•R Example – Finding Model-Free Policy

•R Example – Finding Model-Free Policy (Continued)

•R Example – Validating Model-Free Policy

•R Example – Moving a Pawn with Changed Parameters

•Environment and Q-Learning Functions with R

•Learning Episode and State-Action Functions in R

•State-Action-Reward-State-Action (SARSA)

•Simulated Annealing – An Alternative to Q-Learning

•Q-Learning with a Discount Factor

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We Provide advanced Software trainings. we believe in training the students with the latest technologies, methodologies and advancements in the IT industry. Our mission is to ease the transition from education to the professional world by providing a path of continuous learning and mentorship for students.

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