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    Hands - On Reinforcement Learning with Python

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    Udemy

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    Mode

    Online

    Fees

    ₹ 449 3499

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 449  ₹3,499
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The 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 CartPole-v0 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 CartPole-v0

    The Multi-Armed Bandit

    • Creating a Bandit with 4 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
    • Training the Agent, and Understanding What It Learned

    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 Web-Based GridWorld Visualization

    Markov Decision Process and Neural Networks

    • Understanding Markov Decision Process and Dynamic Programming in CartPole-v0
    • Crafting a Neural Network Using TensorFlow
    • Crafting a Neural Network to Predict the Value of Being in Different Environment
    • Training the Agent in CartPole-v0
    • Visualizing and Understanding How Your Software Agent Has Performed

    Model-Free 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 Epsilon-Greedy Policy
    • Visualizing the Outcomes of the Epsilon-Greedy Policy

    Model-Free 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 Epsilon-Greedy Policy
    • Visualizing the Outcomes of the SARSA

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