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    • Reinforcement Learning with R Algorithms Agents Environment

    Reinforcement Learning with R: Algorithms-Agents-Environment

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    Mode

    Online

    Fees

    ₹ 499 1999

    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
    ₹ 499  ₹1,999
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    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 2 x 2 Grid
    • R Example – Updating Optimal Policy Navigating 2 x 2 Grid
    • R Example – MDPtoolbox Solution Navigating 2 x 2 Grid
    • More MDPtoolbox Function Examples Using R
    • 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

    • Install RStudio
    • Install Python
    • Launch Jupyter Notebook
    • Learning Type Distinctions
    • Get Started with Reinforcement Learning
    • Real-world 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 Reward-Based Learning in R

    • The Course Overview
    • 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
    • Policy Evaluation and Iteration
    • R Example – Moving a Pawn with Changed Parameters
    • Discount Factor and Policy Improvement
    • Monte Carlo Methods
    • 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
    • Visual Q-Learning Examples

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