Reinforcement Learning with Python Explained for Beginners

Learn reinforcement learning right from the beginning

About This Video

• Learn all theories related to reinforcement learning
• Master learning models include model-free learning, Q learning, and temporal difference learning.
• Model uncertainty in the environment, environment stochastic policy, and environment value functions

Although reinforcement learning was first introduced academically many decades ago, recent developments in the field have been amazing. Domains like self-driving cars and natural language processing, the healthcare industry, and online recommender systems have all seen how RL-based agents can make huge gains.

This course will help to get you started with reinforcement learning by first establishing your motivation for this field. Then, we will cover all the essential topics such as Markov Decision Processes and policy and rewards, model-free and temporal difference learning, policy and incentives, policy and rewards, policy and reward, Markov Decision Processes and other important topics.

Each topic is accompanied with exercises and supporting analysis that will help you develop practical and tangible coding skills.

This course will equip you with the knowledge and skills to implement RL in your projects. You will also be able to create a Frozenlake project with the OpenAI Gym toolkit.