These are the exercise files used for NICF – Practical Reinforcement Learning for Beginners course.
The course outline can be found in
https://www.tertiarycourses.com.sg/wsq-reinforcement-learning-course.html
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 Model-Based Learning
- • What is Model-Based Learnings
- • Model-Based Q-Learning Algorithms
Topic 5 Policy Valued Learning
- Policy Based Methods
- Policy Gradient Algorithm
- Implementation of Policy Gradient Algorithm
Topic 6 Overview of Advanced RL Algorithms
- Limitation of Value and Policy-Based Learnings
- Actor-Critic Algorithms
- Deep Reinforcement Algorithms
Final Assessment
- Written Assessment(Q&A)
- Practicum Performance