TechnologyBeginner

Reinforcement Learning: From Bandits to Monte Carlo Methods

Name: Reinforcement Learning: From Bandits to Monte Carlo Methods
Price: 499 INR
Availability: InStock

This course introduces the fundamental concepts and algorithms of reinforcement learning. You will learn how agents interact with environments to maximize reward, covering the reinforcement learning problem, multi-arm bandits, finite Markov decision processes, dynamic programming, and Monte Carlo methods. Through examples and exercises, you will build a solid foundation for understanding and implementing core RL techniques.

What You'll Learn

Explain the key elements of reinforcement learning: policies, reward signals, value functions, and models.

Formulate problems as Markov decision processes and identify states, actions, and rewards.

Apply dynamic programming methods such as policy iteration and value iteration to solve MDPs.

Implement and analyze multi-arm bandit algorithms, including epsilon-greedy and UCB.

Use Monte Carlo methods for policy evaluation and control without requiring a model.

Balance exploration and exploitation in reinforcement learning tasks.

About this course

Reinforcement Learning: From Bandits to Monte Carlo Methods is a self-paced beginner-level technology online course on GreyAcademy, taught by Anurag Trivedi. It has 2 modules and 13 chapters, and it takes about 10 hours to complete. Enrol once and learn at your own pace. You get a completion certificate when you finish all the lessons.

Topics covered: The Reinforcement Learning Problem, Tabular Solution Methods, Multi-arm Bandits, Finite Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal-Difference Learning, Eligibility Traces, Planning and Learning with Tabular Methods, On-policy Approximation of Action Values, Policy Approximation, Applications and Case Studies and more.

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