
Explore reinforcement learning fundamentals where an agent uses states, actions, rewards, and return to learn. Discover Q-learning and Td3, including Q-values, Bellman updates, and the move toward policy gradient.
Explore the policy learning part of twin delayed ddpg, showing how the actor updates via gradient ascent using the critic's q-value and how polyak averaging and delayed updates stabilize learning.
Step four of the td3 training tutorial samples transitions from the replay buffer to create four batches—states, next states, actions, rewards and dones—and converts them to torch tensors.
Implement step ten by feeding the current state and action into two critic models to yield Q1 and Q2 and prepare their mean squared error loss against the target.
Train and evaluate a TD3 agent over 500,000 time steps using replay memory and off-policy learning to improve average rewards across episodes. Start with 10,000 random actions before policy-driven exploration.
Explore how backpropagation simultaneously adjusts all neural network weights via forward and backward passes, detailing weight initialization, learning rate, and batch or epoch training.
Explore policy versus plan in a stochastic Markov decision process, showing how randomness and the Bellman equation reshape state values and drive learned policies over preplanned paths.
Explore q-learning in a gridworld maze in the artificial intelligence 2.0 course, visualize q-values and learned policy, and see how exploration, randomness, and discounting shape reinforcement learning outcomes.
Welcome to Artificial Intelligence 2.0!
In this course, we will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG or TD3, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field).
To approach this model the right way, we structured the course in three parts:
Part 1: Fundamentals
In this part we will study all the fundamentals of Artificial Intelligence which will allow you to understand and master the AI of this course. These include Q-Learning, Deep Q-Learning, Policy Gradient, Actor-Critic and more.
Part 2: The Twin-Delayed DDPG Theory
We will study in depth the whole theory behind the model. You will clearly see the whole construction and training process of the AI through a series of clear visualization slides. Not only will you learn the theory in details, but also you will shape up a strong intuition of how the AI learns and works. The fundamentals in Part 1, combined to the very detailed theory of Part 2, will make this highly advanced model accessible to you, and you will eventually be one of the very few people who can master this model.
Part 3: The Twin-Delayed DDPG Implementation
We will implement the model from scratch, step by step, and through interactive sessions, a new feature of this course which will have you practice on many coding exercises while we implement the model. By doing them you will not follow passively the course but very actively, therefore allowing you to effectively improve your skills. And last but not least, we will do the whole implementation on Colaboratory, or Google Colab, which is a totally free and open source AI platform allowing you to code and train some AIs without having any packages to install on your machine. In other words, you can be 100% confident that you press the execute button, the AI will start to train and you will get the videos of the spider and humanoid running in the end.
So are you ready to embrace AI at full power?
Come join us, never stop learning, and enjoy AI!