Deep Reinforcement Learning Course v2.0

This article is part of the Deep Reinforcement Learning Course v2.0🕹️. A free course from beginner to expert with Tensorflow and PyTorch. Check the syllabus here.

Before studying this new chapter, you should master the elements that we spoke about in the first chapter. If it’s not the case, you can check the video version here or the article version there.

In the first chapter of this course, we learned about what is Reinforcement Learning (RL), the RL process, and the different methods to solve an RL problem.

So today, we’re going to dive deeper into one of these methods: value-based-methods and learn about our first RL algorithm: Q-Learning. …

This article is part of Deep Reinforcement Learning Course. A free course from beginner to expert. Check the syllabus here.

Welcome to the most fascinating topic in Artificial Intelligence: Deep Reinforcement Learning.

Deep RL is a type of Machine Learning where an agent learns how to behave in an environment by performing actions and seeing the results.

Since 2013 and the Deep Q-Learning paper, we’ve seen a lot of breakthroughs. From OpenAI five that beat some of the best Dota2 players of the world, to the Dexterity project, we live in an exciting moment in Deep RL research.

🎉 I’m happy to announce the launch of the new version of the Deep Reinforcement Learning Course 🥳, a free course from beginner to expert where you learn to master the skills and architectures you need, to become a deep reinforcement learning expert with Tensorflow and PyTorch.

Since the launch of the first version in 2018, we had more than 40,000 claps, 2,500 GitHub stars.

Since then, a lot of breakthroughs happened in Deep RL. New libraries were published and some of our implementations become obsolete. …

Unity-ML Agents Course

This article is the third chapter of a new free course on Deep Reinforcement Learning with Unity. Where we’ll create agents that learn to play video games using the Unity game engine 🕹. Check the syllabus here.

If you never study Deep Reinforcement Learning before, you need to check the free course Deep Reinforcement Learning with Tensorflow.

In the last two articles, you learned to use ML-Agents and trained two agents. The first was able to jump over walls, and the second learned to destroy a pyramid to get the golden brick. It’s time to do something harder.

When I was thinking about creating a custom environment, I remembered the famous scene in Indiana Jones, where Indy needs to get the golden statue and avoid a lot of traps to survive. …

Unity-ML Agents Course

This article is the second chapter of a new free course on Deep Reinforcement Learning with Unity. Where we’ll create agents with TensorFlow that learn to play video games using the Unity game engine 🕹. Check the syllabus here.

If you never study Deep Reinforcement Learning before, you need to check the free course Deep Reinforcement Learning with Tensorflow.

Last time, we learned about how Unity ML-Agents works and trained an agent that learned to jump over walls.

Unity-ML Agents Course

This article is part of a new free course on Deep Reinforcement Learning with Unity. Where we’ll create agents with TensorFlow that learn to play video games using the Unity game engine 🕹. Check the syllabus here.

If you never study Deep Reinforcement Learning before, you need to check the free course Deep Reinforcement Learning with Tensorflow.

The past few years have witnessed breakthroughs in reinforcement learning (RL). …

Last time, we learned about curiosity in deep reinforcement learning. The idea of curiosity-driven learning is to build a reward function that is intrinsic to the agent (generated by the agent itself). That is, the agent is a self-learner, as he is both the student and its own feedback teacher.

To generate this reward, we introduce the intrinsic curiosity module (ICM). But this technique has serious drawbacks because of the noisy TV problem, which we’ll introduce here.

So today, we’ll study curiosity-driven learning through random network distillation used in the paper Exploration by Random Network Distillation, and you’ll learn how to implement a PPO agent playing Montezuma’s Revenge with only curiosity as reward. …

In the last few years, we’ve seen a lot of breakthroughs in reinforcement learning (RL). From 2013 with the first deep learning model to successfully learn a policy directly from pixel input using reinforcement learning to the OpenAI Dexterity project in 2019, we live in an exciting moment in RL research.

Today, we’ll learn about curiosity-driven learning methods, one of the most promising series of strategies in deep reinforcement learning. Thanks to this method, we were able to successfully train an agent that wins the first level of Super Mario Bros with only curiosity as a reward.