In reinforcement learning, your system learns how to interact intuitively with the environment by basically doing stuff and watching what happens – but obviously, there’s a lot more to it.

If you’re interested in RL, this article will provide you with a ton of new content to explore this concept. A lot of work has been done with reinforcement learning in the past few years, and I’ve collected some of the most interesting articles, videos, and use cases presenting different concepts, approaches, and methods.

In this list, you’ll find:

• reinforcement learning tutorials,
• examples of where to apply reinforcement learning,
• interesting reinforcement learning projects,
• courses to master reinforcement learning.

All this content will help you go from RL newbie to RL pro.

Reinforcement learning tutorials

**1. **RL with Mario Bros – Learn about reinforcement learning in this unique tutorial based on one of the most popular arcade games of all time – Super Mario.

**2. **Machine Learning for Humans: Reinforcement Learning – This tutorial is part of an ebook titled ‘Machine Learning for Humans’. It explains the core concept of reinforcement learning. There are numerous examples, guidance on the next step to follow in the future of reinforcement learning algorithms, and an easy-to-follow figurative explanation.

**3. **An introduction to Reinforcement Learning – There’s a lot of knowledge here, explained with much clarity and enthusiasm. It starts with an overview of reinforcement learning with its processes and tasks, explores different approaches to reinforcement learning, and ends with a fundamental introduction of deep reinforcement learning.

**4. **Reinforcement Learning from scratch– This article will take you through the author’s process of learning RL from scratch. The author has a lot of knowledge of deep reinforcement learning from working at Unity Technologies. Even beginners will be able to understand his overview of the core concepts of reinforcement learning.

**5. **Deep Reinforcement Learning for Automated Stock Trading– Here you’ll find a solution to a stock trading strategy using reinforcement learning, which optimizes the investment process and maximizes the return on investment. The article includes a proper explanation of three combined algorithms: Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The best of each algorithm is coordinated to provide a solution to optimized stock trading strategies.

**6. **Applications of Reinforcement Learning in Real World– Explore how reinforcement learning frameworks are undervalued when it comes to devising decision-making models. A detailed study of RL applications in real-world projects, explaining what a reinforcement learning framework is, and listing its use-cases in real-world environments. It narrows down the applications to 8 areas of learning, consisting of topics like machine learning, deep learning, computer games, and more. The author also explores the relationship of RL with other disciplines and discusses the future of RL.

#reinforcement learning #machine-learning