Modern Reinforcement Learning: Actor-Critic Methods Udemy Coupon ED How to Implement Cutting Edge Artificial Intelligence Research Papers in the Open AI Gym Using the PyTorch Framework Get Udemy Course 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 Description 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 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.
Sep-3-2020, 10:13:13 GMT