Build Artificial Intelligence (AI) agents using Reinforcement Learning in PyTorch: DQN, A2C, Policy Gradients, +More!
Sub Category
- Data Science
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Objectives
- Review Reinforcement Learning Basics: MDPs, Bellman Equation, Q-Learning
- Theory and Implementation of Deep Q-Learning / DQN
- Theory and Implementation of Policy Gradient Methods and A2C (Advantage Actor-Critic)
- Apply DQN and A2C to Atari Environments (Breakout, Pong, Asteroids, etc.)
- VIP Only: Apply A2C to Build a Trading Algorithm for Multi-Period Portfolio Optimization
Pre Requisites
- Reinforcement Learning fundamentals: MDPs, Bellman Equation, Monte Carlo Methods, Temporal Difference Learning
- Undergraduate STEM math: calculus, probability, statistics
- Python programming and numerical computing (Numpy, Matplotlib, etc.)
- Deep Learning fundamentals: Convolutional neural networks, hyperparameter optimization, etc.
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!
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