Skip to main content
HomeArtificial IntelligenceDeep Reinforcement Learning in Python

Deep Reinforcement Learning in Python

Learn and use powerful Deep Reinforcement Learning algorithms, including refinement and optimization techniques.

Start Course for Free
4 Hours15 Videos49 Exercises

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
GroupTraining 2 or more people?Try DataCamp For Business

Loved by learners at thousands of companies


Course Description

Discover the cutting-edge techniques that empower machines to learn and interact with their environments. You will dive into the world of Deep Reinforcement Learning (DRL) and gain hands-on experience with the most powerful algorithms driving the field forward. You will use PyTorch and the Gymnasium environment to build your own agents.

Master the Fundamentals of Deep Reinforcement Learning

Our journey begins with the foundations of DRL and their relationship to traditional Reinforcement Learning. From there, we swiftly move on to implementing Deep Q-Networks (DQN) in PyTorch, including advanced refinements such as Double DQN and Prioritized Experience Replay to supercharge your models. Take your skills to the next level as you explore policy-based methods. You will learn and implement essential policy-gradient techniques such as REINFORCE and Actor-Critic methods.

Use Cutting-edge Algorithms

You will encounter powerful DRL algorithms commonly used in the industry today, including Proximal Policy Optimization (PPO). You will gain practical experience with the techniques driving breakthroughs in robotics, game AI, and beyond. Finally, you will learn to optimize your models using Optuna for hyperparameter tuning. By the end of this course, you will have acquired the skills to apply these cutting-edge techniques to real-world problems and harness DRL's full potential!
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more
Try DataCamp for BusinessFor a bespoke solution book a demo.
  1. 1

    Introduction to Deep Reinforcement Learning

    Free

    Discover how deep reinforcement learning improves upon traditional Reinforcement Learning while studying and implementing your first Deep Q Learning algorithm.

    Play Chapter Now
    Introduction to deep reinforcement learning
    50 xp
    Environment and neural network setup
    100 xp
    DRL training loop
    100 xp
    Introduction to deep Q learning
    50 xp
    Deep learning and DQN
    50 xp
    The Q-Network architecture
    100 xp
    Instantiating the Q-Network
    100 xp
    The barebone DQN algorithm
    50 xp
    Barebone DQN action selection
    100 xp
    Barebone DQN loss function
    100 xp
    Training the barebone DQN
    100 xp
  2. 2

    Deep Q-learning

    Dive into Deep Q-learning by implementing the original DQN algorithm, featuring Experience Replay, epsilon-greediness and fixed Q-targets. Beyond DQN, you will then explore two fascinating extensions that improve the performance and stability of Deep Q-learning: Double DQN and Prioritized Experience Replay.

    Play Chapter Now
  3. 3

    Introduction to Policy Gradient Methods

    Learn about the foundational concepts of policy gradient methods found in DRL. You will begin with the policy gradient theorem, which forms the basis for these methods. Then, you will implement the REINFORCE algorithm, a powerful approach to learning policies. The chapter will then guide you through Actor-Critic methods, focusing on the Advantage Actor-Critic (A2C) algorithm, which combines the strengths of both policy gradient and value-based methods to enhance learning efficiency and stability.

    Play Chapter Now
  4. 4

    Proximal Policy Optimization and DRL Tips

    Explore Proximal Policy Optimization (PPO) for robust DRL performance. Next, you will examine using an entropy bonus in PPO, which encourages exploration by preventing premature convergence to deterministic policies. You'll also learn about batch updates in policy gradient methods. Finally, you will learn about hyperparameter optimization with Optuna, a powerful tool for optimizing performance in your DRL models.

    Play Chapter Now
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

Collaborators

Collaborator's avatar
James Chapman
Collaborator's avatar
Jasmin Ludolf
Collaborator's avatar
Francesca Donadoni

Audio Recorded By

Timothée Carayol's avatar
Timothée Carayol

Prerequisites

Intermediate Deep Learning with PyTorchReinforcement Learning with Gymnasium in Python
Timothée Carayol HeadshotTimothée Carayol

Principal Machine Learning Engineer

Timothée Carayol has been a passionate practitioner of data science and machine learning since 2010. Formerly a Research Data Scientist at Meta working on AI Infrastructure Analytics, Timothée recently took up a new challenge as Principal Machine Learning Engineer at Komment, where he helps build the future of software documentation.
See More

What do other learners have to say?

FAQs

Join over 14 million learners and start Deep Reinforcement Learning in Python today!

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.