Modern Reinforcement Learning: Actor-Critic Methods 4.2 (8 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
What you’ll learn
- How to code policy gradient methods in PyTorch
- How to code Deep Deterministic Policy Gradients (DDPG) in PyTorch
- How to code Twin Delayed Deep Deterministic Policy Gradients (TD3) in PyTorch
- How to code actor critic algorithms in PyTorch
- How to implement cutting edge artificial intelligence research papers in Python
In this advanced course on deep reinforcement learning, you will learn how to implement policy gradient, actor critic, deep deterministic policy gradient (DDPG), and twin delayed deep deterministic policy gradient (TD3) algorithms in a variety of challenging environments from the Open AI gym.
The course begins with a practical review of the fundamentals of reinforcement learning, including topics such as:
- The Bellman Equation
- Markov Decision Processes
- Monte Carlo Prediction
- Monte Carlo Control
- Temporal Difference Prediction TD(0)
- Temporal Difference Control with Q Learning
And moves straight into coding up our first agent: a blackjack playing artificial intelligence. From there we will progress to teaching an agent to balance the cart pole using Q learning.
After mastering the fundamentals, the pace quickens, and we move straight into an introduction to policy gradient methods. We cover the REINFORCE algorithm, and use it to teach an artificial intelligence to land on the moon in the lunar lander environment from the Open AI gym. Next we progress to coding up the one step actor critic algorithm, to again beat the lunar lander.
With the fundamentals out of the way, we move on to our harder projects: implementing deep reinforcement learning research papers. We will start with Deep Deterministic Policy Gradients, which is an algorithm