# The Ultimate AI & Reinforcement Learning Training Course

## The Ultimate AI & Reinforcement Learning Training Course

The best **reinforcement learning course** online that will help you understand the foundations of modern probabilistic **artificial intelligence (AI)**,

### Go Link On Udemy

#### What you'll learn

- Reinforcement Learning Basics
- Understand the motivation for reinforcement learning
- Learn how to manage and install software for machine
- Learn how to implement common RL algorithms
- Learn to Generate a Random MDP Problem
- Learn how to solve various reinforcement learning problems
- Learn how to model uncertainty of the environments
- Solve Markov Decision Processes

### Welcome to this course.

Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. It is a part of machine learning. Reinforcement learning is one powerful paradigm for making good decisions, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. Building on a strong theoretical foundation, this course takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Reinforcement learning allows machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance.

Starting with an introduction to RL, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package.

#### In this course, you'll learn

- Reinforcement Learning Basics
- Understand the motivation for reinforcement learning
- Learn how to manage and install software for machine
- Learn how to implement common RL algorithms
- Learn to Generate a Random MDP Problem
- Learn how to solve various reinforcement learning problems
- Learn how to model uncertainty of the environments
- Solve Markov Decision Processes
- Execute the Frozenlake project using the OpenAI Gym toolkit

By the end of this course, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.