Sample-based Learning Methods

Go to Course: https://www.coursera.org/learn/sample-based-learning-methods

Introduction

# Review and Recommendation: Sample-Based Learning Methods Course on Coursera If you’re looking to delve deeper into the world of reinforcement learning, I highly recommend the “Sample-Based Learning Methods” course offered on Coursera, a part of the Reinforcement Learning Specialization provided by the University of Alberta and Onlea. This course is meticulously designed to guide learners through the intricacies of sample-based algorithms that enable agents to learn optimal policies through trial-and-error experiences, minimizing the need for prior knowledge of the environment. ## Overview The course provides an engaging and comprehensive overview of several key algorithms, including Monte Carlo methods and temporal difference learning methods such as Q-learning. What sets this course apart is its emphasis on hands-on learning where students gain insights into how agents can interact with their environment to learn from their own experiences. This approach is not just theoretical; it provides a stimulating pathway to achieving optimal behavior within diverse environments. ## Course Syllabus Breakdown ### Welcome to the Course! The introductory module sets the tone perfectly, welcoming students with an opportunity to connect with instructors and peers. Engaging in the "Meet and Greet" section allows learners to build community which can enhance the overall educational experience. ### Week 1: Monte Carlo Methods for Prediction & Control In the first week, learners are introduced to the foundational concepts of estimating value functions and optimal policies using Monte Carlo methods. Understanding on-policy and off-policy methods is tackled adeptly, emphasizing both practical and theoretical perspectives. This module revisits the exploration problem, making it relevant to broader reinforcement learning contexts. ### Week 2: Temporal Difference Learning Methods for Prediction The second segment delves deeper into temporal difference (TD) learning. This week is particularly essential as students discover how TD learning serves as a bridge between Monte Carlo and dynamic programming methods. It emphasizes the strength of bootstrapping, enabling live learning from agent-world interactions without requiring a complete model of the environment. This is particularly beneficial for learners who want to implement real-time solutions in their projects. ### Week 3: Temporal Difference Learning Methods for Control The third week kicks up the complexity level by exploring control mechanisms within TD learning. By learning algorithms like Sarsa, Q-learning, and Expected Sarsa, students gain practical experience in implementing these concepts in simulated environments, such as the well-known Cliff World scenario. This hands-on approach reinforces theoretical comprehension, making it easier to internalize complicated concepts. ### Week 4: Planning, Learning & Acting In the final week, the course elegantly ties together the concepts of model-based and model-free learning through the Dyna architecture. This innovative framework allows for the simulation of hypothetical experiences from learned models, enriching sample efficiency across various learning strategies. Understanding how to create robust learning systems resistant to inaccuracies in models is an invaluable skill that can be applied in real-world scenarios. ## Key Highlights - **Clear Instruction:** The instructors break down complex concepts into manageable sections, with a mix of theoretical insights and practical applications. - **Hands-On Learning:** Each module comes with practical assignments that reinforce the lectures, allowing students to apply what they learn immediately. - **Community Engagement:** The course encourages learners to interact, fostering a collaborative learning environment that enhances understanding and retention. - **Flexible Learning:** Coursera's platform allows you to learn at your own pace, making it easier to fit this course into your schedule. ## Recommendation I wholeheartedly recommend the “Sample-Based Learning Methods” course to anyone interested in machine learning and reinforcement learning. Whether you are a beginner looking to establish a base in these concepts or an experienced practitioner wanting to deepen your understanding of sample-based learning, this course is tailored to meet those needs. The combination of rigorously structured content, engaging teaching methods, and practical application make it a must-have experience for aspiring data scientists, AI researchers, and machine learning engineers. Completing this course will not only enhance your theoretical understanding but also equip you with practical skills that are highly applicable in various fields, including robotics, game development, and artificial intelligence. Enroll today, and take the first step towards mastering reinforcement learning!

Syllabus

Welcome to the Course!

Welcome to the second course in the Reinforcement Learning Specialization: Sample-Based Learning Methods, brought to you by the University of Alberta, Onlea, and Coursera. In this pre-course module, you'll be introduced to your instructors, and get a flavour of what the course has in store for you. Make sure to introduce yourself to your classmates in the "Meet and Greet" section!

This week you will learn how to estimate value functions and optimal policies, using only sampled experience from the environment. This module represents our first step toward incremental learning methods that learn from the agent’s own interaction with the world, rather than a model of the world. You will learn about on-policy and off-policy methods for prediction and control, using Monte Carlo methods---methods that use sampled returns. You will also be reintroduced to the exploration problem, but more generally in RL, beyond bandits.

This week, you will learn about one of the most fundamental concepts in reinforcement learning: temporal difference (TD) learning. TD learning combines some of the features of both Monte Carlo and Dynamic Programming (DP) methods. TD methods are similar to Monte Carlo methods in that they can learn from the agent’s interaction with the world, and do not require knowledge of the model. TD methods are similar to DP methods in that they bootstrap, and thus can learn online---no waiting until the end of an episode. You will see how TD can learn more efficiently than Monte Carlo, due to bootstrapping. For this module, we first focus on TD for prediction, and discuss TD for control in the next module. This week, you will implement TD to estimate the value function for a fixed policy, in a simulated domain.

This week, you will learn about using temporal difference learning for control, as a generalized policy iteration strategy. You will see three different algorithms based on bootstrapping and Bellman equations for control: Sarsa, Q-learning and Expected Sarsa. You will see some of the differences between the methods for on-policy and off-policy control, and that Expected Sarsa is a unified algorithm for both. You will implement Expected Sarsa and Q-learning, on Cliff World.

Up until now, you might think that learning with and without a model are two distinct, and in some ways, competing strategies: planning with Dynamic Programming verses sample-based learning via TD methods. This week we unify these two strategies with the Dyna architecture. You will learn how to estimate the model from data and then use this model to generate hypothetical experience (a bit like dreaming) to dramatically improve sample efficiency compared to sample-based methods like Q-learning. In addition, you will learn how to design learning systems that are robust to inaccurate models.