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Complete guide to Reinforcement Learning, Markov Decision Process, Q-Learning, applications using Python & OpenAI GYM
An excellent training about Data Science
Reinforcement Learning with Python Explained for Beginners
Reinforcement Learning (RL) possesses immense potential and is doubtless one of the most dynamic and stimulating fields of research in Artificial Intelligence. RL is considered as a game-changer in Data Science, particularly after observing the winnings of AI agents AlphaGo Zero and OpenAI Five against top human champions. However, RL is not restricted to games. The progress in Reinforcement Learning, especially during the last few years, has been sensational. RL is everywhere now, ranging from resource management to chemistry, from healthcare to finance, and from Recommender Systems to more advanced applications in stock prediction. Since RL is goal-oriented learning, an understanding of RL is not only vital but also indispensable in all the fields of Data Science. This course will enable you to take your career to the next level, as it presents you with a clear explanation of the concepts and implementations of RL in Data Science. The course Reinforcement Learning, Theory and Practice provides you with an opportunity for innovative, independent learning. The course focuses on the practical applications of RL and includes a hands-on project. The course is: Easy to understand. Descriptive. Comprehensive. Practical with live coding. Rich with advanced and the most recently discovered RL models by the champions in this field. This course is designed for beginners, although complex concepts are covered later. As this course is a compilation of all the basics, it will inspire you to move forward and experience much more than what you have learned. You will be assigned homework/ tasks/ activities at the end of each module, which will assess / (further build) your learning based on the concepts and methods you have learned earlier on. Since the aim is to get you up and running with implementations, many of these activities will be coding based. Data Science is unquestionably a rewarding career. You get to solve some of the most interesting problems, and you are rewarded with a handsome salary package. A core understanding of RL will empower you with more AI tools and ensure progressive career growth. As we have already said, RL possesses immense potential. Dont miss out on this opportunity to learn the advanced concepts and methodologies of RL at a highly competitive price. The tutorials are subdivided into 75+ short HD videos along with detailed code notebooks. Teaching is our passion: Our online tutorials have been created with the best possible expertise to help you in understanding the RL concepts clearly. We have taken great care to ensure the code base is up to date. We really want you to accomplish a strong basic understanding of RL before you move onward to the advanced version. The perks of this compelling course include high-quality video content, assessment questions, meaningful course material, course notes, and handouts. You can also approach our team whenever you have any queries. Course Content: This all-inclusive course consists of the following topics:1. Introductiona. Motivationi. What is Reinforcement Learning?ii. How is it different from other Machine Learning Frameworks?iii. Real-world examplesiv. Exercises and Thoughtsb. Terminology of Reinforcement Learningi. Agentii. Environmentiii. Actioniv. Statev. Transitionvi. Rewardvii. Policyviii. Exercises and Thoughtsc. Example Grid Worldi. Deterministic Worldii. Stochastic Worldiii. Stationary Worldiv. Non-Stationary Worldv. Exercises and Thoughts2. Markov Decision Process (MDP)a. Prerequisitesi. Probability Theory Reviewii. Modeling Uncertainty of Environmentiii. Running Averagesiv. Simulation in Pythonv. Exercises and Thoughtsb. Elements of an MDPi. Input: State Spaceii. Input: Action Spaceiii. Input: Environment Modeliv. Input: Reward functionv. Output: Policyvi. Worked Examplesvii. Exercises and Thoughtsc. More on Rewardsi. Delayed Rewardii. Reward Scalingiii. Policy Changes with Reward Scaling: Worked Exampleiv. Infinite Horizons and Stationarityv. Walks or Sequencesvi. Value of a Walkvii. Stationarity of Preferencesviii. Discounted Rewardsix. Exercises and Thoughtsd. Solving an MDPi. Bellman Optimization Criteriaii. Model-Based Value Iterationsiii. Optimal Value Functioniv. Finding Optimal Policyv. Model-Based Policy Iterationsvi. Action-Value Functionsvii. Relationship Between Value Functions and Action-Value Functionsviii. Policy Evaluationix. Learner Evaluationx. Exercises and Thoughts3. Model Free Learninga. Value Approximationi. Episodesii. Running-Averages Applicationsiii. Incremental Learningiv. Properties of Learning Ratesv. Simulation in Pythonvi. Exercises and Thoughtsb. Temporal Difference (TD) Learningi. What is Temporal Difference?ii. TD (1) Update Ruleiii. Eligibility Tracesiv. TD (1) Learning Algorithmv. Implementation in Pythonvi. Limitations of TD (1)vii. Exercises and Thoughtsc. Toward TD()i. Maximum Likelihood Estimateii. TD (0) Update Ruleiii. TD ()iv. K-Step Look-a-headv. Combinations of Different Step Look-a-headsvi. Good Values of vii. TD () Algorithmviii. Implementation in Pythonix. Exerc
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