Deep RL & Sequential Decision Making: Master Q-Learning, Policy Gradients, DQN, and PPO Implementation for Certification
Sub Category
- Other IT & Software
{inAds}
Objectives
- Explain the core components of a Markov Decision Process (MDP), the Bellman equations, and the concept of optimal policies.
- Implement and compare classic tabular methods, including Q-Learning and SARSA, for small-scale environment solving.
- Apply Dynamic Programming techniques (Policy Iteration and Value Iteration) to solve environments with known dynamics.
- Design and implement Deep Q-Networks (DQN) using crucial stability features like experience replay and target networks.
- Master Policy Gradient methods, including the REINFORCE algorithm and techniques for variance reduction (baseline subtraction).
- Understand the necessity of function approximation and utilize neural networks to handle large or continuous state spaces.
- Implement advanced, stable Actor-Critic algorithms like A2C, A3C, and Soft Actor-Critic (SAC).
Pre Requisites
- Solid understanding of Python programming (intermediate level is essential).
- Familiarity with foundational concepts of calculus, linear algebra, and probability theory.
- Basic experience with machine learning and deep learning frameworks (PyTorch or TensorFlow).
- Access to a computer capable of running deep learning models for practical exercises.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
{inAds}
Coupon Code(s)