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Hands On Reinforcement Learning With Python

Course

HANDS ON REINFORCEMENT LEARNING WITH PYTHON

Category

Python Professional Training

Eligibility

Job Aspirants

Mode

Both Classroom and Online Classes

Batches

Week Days and Week Ends

Duration :

30 to 45 days

Python What will you learn?

•Basic to Advance concepts of Python
•Build and run your first application in Python.
•Learn how to write high-quality code using Python.
•Cover all basic Concepts with in-depth description of Python.
•Learn about Python in a step by step approach
•You can learn Python to code like a pro!
•An easy way to learn one of the widely used Python
•In This Course u Will Learn How To Develop Apps using Python
•Python -Learn how to use one component inside an other i.e complex components.

hands on reinforcement learning with python Training Highlights

•Post training offline support available
•Certificate after completion of the course
•Accessibility of adequate training resources
•We enage Experienced trainers for Quality Training
•Highly Experienced Trainer with 10+ Years in MNC Company
•Project manager can be assigned to track candidates’ performance
•Training time :  Week Day / Week End – Any Day Any Time – Students can come and study
•Lifetime access to our 24×7 online support team who will resolve all your technical queries, through ticket based tracking system.

Who are eligible for Python

•Architect, Lead, Developer, Project Manager, Verification Engineer, Rtl Design, Physical Design, L3 Support Engineer, Cloud Computing, Big Data Engineer
•Java Developer, Manual Testing, Automation Testing, Oracle Developer, Sybase Developer, SQL Server Developer with SSIS and SSRS, Windows/Weblogic Application
•Javascript, CSS, UI Development, Html5, JSON, MySQL, Spring Boot, Design Patterns, NoSQL, Algorithms, Ui Developer
•Qa, Ui/ux, Java Developer, Java Architect, C++/qt, Php, Lamp, Api, J2ee, Java, Soa, Esb, Middleware, Bigdata Achitect, Hadoop Architect, Deep
•Web Application Developers, Java Developers, DBA LEAD, DBA Manager, Asset Control developer, embedded software engineer, oracle applications technical

HANDS ON REINFORCEMENT LEARNING WITH PYTHON Syllabus

•Getting Started With Reinforcement Learning Using OpenAI Gym
•The Course Overview
•Understanding Reinforcement Learning Algorithms
•Installing and Setting Up OpenAI Gym
•Running a Visualization of the Cart Robot CartPolev in OpenAI Gym
•Lights Camera Action Building Blocks of Reinforcement Learning
•Exploring the Possible Actions of Your CartPole Robot in OpenAI Gym
•Understanding the Environment of CartPole in OpenAI Gym
•Coding up Your First Solution to CartPolev
•The MultiArmed Bandit
•Creating a Bandit with Arms Using Python and Numpy
•Creating an Agent to Solve the MAB Problem Using Python and Tensorflow
•Training the Agent and Understanding What It Learned
•The Contextual Bandit
•Creating an Environment with Multiple Bandits Using Python and Numpy
•Creating Your First Policy Gradient Based RL Agent with TensorFlow
•Dynamic Programming Prediction Control and Value Approximation
•Visualizing Dynamic Programming in GridWorld in Your Browser
•Understanding Prediction Through Building a Policy Evaluation Algorithm
•Understanding Control Through Building a Policy Iteration Algorithm
•Building a Value Iteration Algorithm
•Linking It All Together in the WebBased GridWorld Visualization
•Markov Decision Processes and Neural Networks
•Understanding Markov Decision Process and Dynamic Programming in CartPolev
•Crafting a Neural Network Using TensorFlow
•Crafting a Neural Network to Predict the Value of Being in Different Environment
•Training the Agent in CartPolev
•Visualizing and Understanding How Your Software Agent Has Performed
•ModelFree Prediction and Control With Monte Carlo MC
•Running the Blackjack Environment From the OpenAI Gym
•Tallying Every Outcome of an Agent Playing Blackjack Using MC
•Visualizing the Outcomes of a Simple Blackjack Strategy
•Control Building a Very Simple EpsilonGreedy Policy
•Visualizing the Outcomes of the EpsilonGreedy Policy
•ModelFree Prediction and Control with Temporal Difference TD
•Visualizing TD and SARSA in GridWorld in Your Browser
•Running the GridWorld Environment from the OpenAI Gym
•Building a SARSA Algorithm to Find the Optimal EpsilonGreedy Policy
•Visualizing the Outcomes of the SARSA