The Ultimate AI & Reinforcement Learning Training Course
Learn the basics of reinforcement learning and how to implement common RL algorithms. Master the art of training and deploying RL agents. Perfect for beginners in data science and machine learning.
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.
Who this course is for:
- Beginners in the field of data science and machine learning
- Anyone who wants to learn RL
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