Advanced Reinforcement Learning: policy gradient methods

Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: (REINFORCE, A2C, PPO, etc)

Created by: Escape Velocity Labs
Hands-on, comprehensive AI courses

What Will I Learn?

• Master some of the most advanced Reinforcement Learning algorithms.
• Learn how to create AIs that can act in a complex environment to achieve their goals.
• Create from scratch advanced Reinforcement Learning agents using Python's most popular tools (PyTorch Lightning, OpenAI gym, Optuna)
• Learn how to perform hyperparameter tuning (Choosing the best experimental conditions for our AI to learn)
• Fundamentally understand the learning process for each algorithm.
• Debug and extend the algorithms presented.
• Understand and implement new algorithms from research papers.

Requirements

• Be comfortable programming in Python
• Completing our course "Reinforcement Learning beginner to master" or being familiar with the basics of Reinforcement Learning (or watching the leveling sections included in this course).
• Know basic statistics (mean, variance, normal distribution)

Target audience

• Developers who want to get a job in Machine Learning.
• Data scientists/analysts and ML practitioners seeking to expand their breadth of knowledge.
• Robotics students and researchers.
• Engineering students and researchers.