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- 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
Reinforcement Learning with R: Algorithms-Agents-Environment
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
₹ 499 ₹1,999
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
The Course Overview
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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