Cutting-Edge AI: Deep Reinforcement Learning in PyTorch (v2)

Build Artificial Intelligence (AI) agents using Reinforcement Learning in PyTorch: DDPG, TD3, SAC, +More!

What you’ll learn
✓ Review Reinforcement Learning Basics: MDPs, Bellman Equation, Q-Learning
✓ Theory and Implementation of DDPG (Deep Deterministic Policy Gradient)
✓ Theory and Implementation of TD3 (Twin-Delayed DDPG)
✓ Apply DDPG and TD3 to MuJoCo physics simulator environments
✓ VIP Only: Apply DDPG and TD3 to Position Sizing in Algorithmic Trading

Requirements
● 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: neural networks, hyperparameter optimization, etc.

Description
This course contains the use of artificial intelligence (it’s an AI course, duh!).

The world of Artificial Intelligence is moving fast, and Deep Reinforcement Learning (DRL) is the engine driving its most impressive breakthroughs—from mastering complex games to autonomous robotics and high-frequency trading.

Welcome to Version 2. We’ve completely rebuilt this course from the ground up to reflect the modern AI landscape. This isn’t just a minor update; it’s a total transformation designed to take you from a curious coder to a DRL expert.

Why Version 2?

We listened to your feedback and updated every component to ensure you’re learning with the most relevant, industry-standard tools available today

• PyTorch Native: We’ve ditched the clunky syntax of TensorFlow 1 for the elegance and flexibility of PyTorch, the preferred framework for AI researchers worldwide.

• Free MuJoCo Integration: Take advantage of the industry-leading physics engine, MuJoCo, which is now open-source and free to use for your robotics simulations.

• Refined Explanations: We’ve streamlined the theory, making the “math-heavy” concepts intuitive, clear, and actually fun to learn.

What You’ll Master

This course bridges the gap between academic theory and production-ready code. You won’t just learn how to use libraries; you’ll learn how to build these sophisticated agents from scratch.

1. The Foundations (The RL Brain)

Before diving into deep networks, we ensure your foundation is rock-solid. You’ll master Markov Decision Processes (MDPs) and the Bellman Equation – the mathematical heart of how an agent “values” its future.

2. Deep Deterministic Policy Gradient (DDPG)

Learn the algorithm that brought Reinforcement Learning into continuous action spaces. Unlike DQN, which chooses from a list of buttons, DDPG allows agents to operate in worlds with infinite possibilities—like precisely rotating a robotic arm or adjusting a throttle.

3. TD3 (Twin-Delayed DDPG)

Move beyond the basics with TD3, the “corrected” version of DDPG. You’ll learn how to tackle the common problem of overestimation bias using clipped double-Q learning and delayed policy updates, resulting in much more stable and reliable agents.

4. The VIP Project: Algorithmic Trading

Put your skills to the test in a high-stakes environment. You will build a custom environment for Position Sizing in Algorithmic Trading. You’ll code the environment from scratch, then deploy your DDPG and TD3 agents to manage risk and maximize returns in a simulated market.

Key Highlights

• Preliminaries: Gymnasium basics, Vector Environments, and the “Autoreset” paradigm.

• DQN Review: A quick refresher on the architecture that started the DRL revolution.

• DDPG Mastery: Three-part deep dive into the theory and PyTorch implementation.

• State-of-the-Art: TD3 implementation and a sneak peek into Soft Actor-Critic (SAC).

• The VIP Project: End-to-end Algorithmic Trading bot development.

Is This Course For You?

If you are a programmer, data scientist, or AI enthusiast who wants to go beyond “plug-and-play” tutorials and truly understand the mechanics of Deep RL, this is your roadmap. We don’t just show you what buttons to press; we show you how the engine works.

Are you ready to build agents that can learn, adapt, and conquer?

Enroll now and start building the next generation of intelligent systems!

Suggested prerequisites

• calculus

• probability and statistics

• Python coding: if/else, loops, lists, dicts, sets

• Numpy coding: matrix and vector operations, loading a CSV file

• Neural networks, backpropagation, hyperparameter optimization

• Can write a feedforward neural network in PyTorch

• Can write a convolutional neural network in PyTorch

• Markov Decision Proccesses (MDPs)

• Know how to implement Temporal Difference Learning to train RL Agents

Who this course is for
■ Machine Learning & AI enthusiasts who want to explore one of the most exciting fields in AI: reinforcement learning
■ Software developers and engineers looking to build intelligent agents that learn from experience
■ Quantitative finance professionals interested in applying RL to risk management and algorithmic trading
■ Students and researchers studying AI, computer science, or data science who want hands-on experience with real RL implementations
■ Game developers curious about using RL to train AI for complex behaviors and adaptive gameplay
■ Robotics practitioners who want to learn how agents can make sequential decisions in physical environments
■ Data scientists aiming to expand their toolkit beyond supervised learning / unsupervised learning
■ Traders and investors looking to apply cutting-edge AI methods to automated trading strategies
■ Entrepreneurs and hobbyists eager to experiment with advanced AI models and build projects that learn and adapt over time
■ Professionals switching careers into AI/ML and looking for portfolio-ready, real-world projects

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