Practical Reinforcement Learning for Beginners
About This Course
Unlock the transformative power of artificial intelligence with our WSQ-endorsed Practical Reinforcement Learning for Beginners course. As one of the most dynamic fields in AI, reinforcement learning offers unparalleled opportunities for problem-solving and decision-making. This course guides you through the foundational algorithms and techniques, all while offering hands-on experience through real-world projects. You'll develop the skills to implement effective learning agents in various environments.
Upon completing the course, you'll be proficient in the core concepts and applications of reinforcement learning. From designing smart agents to navigating complex data sets, this course equips you with the know-how to apply reinforcement learning in practical scenarios, making it an essential stepping stone for anyone aiming to specialize in AI and data science.
What You'll Learn
- understand and apply the fundamental concepts of reinforcement learning
use RL on OpenAI Gym
- build value-based reinforcement learning systems
- build model-based reinforcement learning systems
- build policy-based reinforcement learning systems
- assess reinforcement learning systems and suggest more advanced reinforcement learning systems
Course Outline:
Topic 1 Introduction to Reinforcement Learning
What is Reinforcement Learning (RL)?
Markov Decision Process (MDP) and RL
Applications of RL
RL Algorithms Classifications
Topic 2 OpenAI Gym
What is OpenAI Gym
Install OpenAI Gym
OpenAI Gym Operations
Topic 3 Value Based Q-Learning
What is Q-Learning
Q Value and Q-Table
Bellman Equation
Q-Learning Algorithm
Epsilon Greedy Explore-Exploit Strategy
On-Policy vs Off-Policy Learning
What is SARSA?
SARSA Algorithm
Topic 4 Policy-Based Learning
Policy Based Methods
Policy Gradient Algorithm
Implementation of Policy Gradient Algorithm
Topic 5 Overview of Advanced RL Algorithms
Limitation of Value and Policy-Based Learnings
Actor-Critic Algorithms
Deep Reinforcement Algorithms
Topic 6 Model-Based Learning
What is Model-Based Learnings
Model-Based Q-Learning Algorithms
Entry Requirements
Knowledge and Skills
• Able to operate computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience.
• Minimum 18 years old