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    Modern Reinforcement Learning: Actor-Critic Algorithms

    BY
    Udemy

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    Lavel

    Expert

    Mode

    Online

    Fees

    ₹ 3099

    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
    ₹ 3,099
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Introduction

    • What You Will Learn in this Course
    • Required Background, Software, and Hardware
    • How to Succeed in this Course

    Fundamentals of Reinforcement Learning

    • Review of Fundamental Concepts
    • Calculating State Transition Probabilities
    • Teaching an AI about Black Jack with Monte Carlo Prediction
    • Teaching an AI How to Play Black Jack with Monte Carlo Control
    • Review of Temporal Difference Learning Methods
    • Teaching an AI about Balance with TD(0) Prediction
    • Teaching an AI to Balance the Cart Pole with Q Learning

    Landing on the Moon with Policy Gradients & Actor Critic Methods

    • What's so Great About Policy Gradient Methods?
    • Combining Neural Networks with Monte Carlo: REINFORCE Policy Gradient Algorithm
    • Introducing the Lunar Lander Environment
    • Coding the Agent's Brain: The Policy Gradient Network
    • Coding the Policy Gradient Agent's Basic Functionality
    • Coding the Agent's Learn Function
    • Coding the Policy Gradient Main Loop and Watching our Agent Land on the Moon
    • Actor Critic Learning: Combining Policy Gradients & Temporal Difference Learning
    • Coding the Actor Critic Networks
    • Coding the Actor Critic Agent
    • Coding the Actor Critic Main Loop and Watching Our Agent Land on the Moon

    Deep Deterministic Policy Gradients (DDPG): Actor Critic with Continuous Actions

    • Getting up to Speed With Deep Q Learning
    • How to Read and Understand Cutting Edge Research Papers
    • Analyzing the DDPG Paper Abstract and Introduction
    • Analyzing the Background Material
    • What Algorithm Are We Going to Implement?
    • What Results Should We Expect?
    • What Other Solutions are Out There?
    • What Model Architecture and Hyperparameters Do We Need?
    • Handling the Explore-Exploit Dilemma: Coding the OU Action Noise Class
    • Giving our Agent a Memory: Coding the Replay Memory Buffer Class
    • Deep Q Learning for Actor Critic Methods: Coding the Critic Network Class
    • Coding the Actor Network Class
    • Giving our DDPG Agent Simple Autonomy: Coding the Basic Functions of Our Agent
    • Giving our DDPG Agent a Brain: Coding the Agent's Learn Function
    • Coding the Network Parameter Update Functionality
    • Coding the Main Loop and Watching Our DDPG Agent Land on the Moon

    Twin Delayed Deep Deterministic Policy Gradients (TD3)

    • Some Tips on Reading this Paper
    • Analyzing the TD3 Paper Abstract and Introduction
    • What Other Solutions Have People Tried?
    • Reviewing the Fundamental Concepts
    • Is Overestimation Bias Even a Problem in Actor-Critic Methods?
    • Why is Variance a Problem for Actor-Critic Methods?
    • What Results Can We Expect?
    • Coding the Brains of the TD3 Agent - The Actor and Critic Network Classes
    • Giving our TD3 Agent Simple Autonomy - Coding the Basic Agent Functionality
    • Giving our TD3 Agent a Brain - Coding the Learn Function
    • Coding the Network Parameter Update Functionality
    • Coding the Main Loop And Watching our Agent Learn to Walk

    Soft Actor Critic

    • A Quick Word on the Paper
    • Getting Acquainted With a New Framework
    • Checking Out What Has Been Done Before
    • Inspecting the Foundation of this New Framework
    • Digging Into the Mathematics of Soft Actor Critic
    • Seeing How the New Algorithm Measures Up
    • Coding the Neural Networks
    • Coding the Soft Actor Critic Basic Functionality
    • Coding the Soft Actor Critic Algorithm
    • Coding the Main Loop and Evaluating Our Agent

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