What will you learn in this Sample-based Learning Methods Course
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Understand Temporal-Difference (TD) learning and Monte Carlo methods as strategies for estimating value functions from sampled experience.
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Implement and apply TD algorithms, including Expected Sarsa and Q-learning, for control tasks.
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Differentiate between on-policy and off-policy control methods.
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Explore planning with simulated experience and implement the Dyna algorithm to enhance learning efficiency
Program Overview
1. Monte Carlo Methods
⏳ 4 hours
Learn about Monte Carlo methods for prediction and control, using sampled returns to estimate value functions without requiring knowledge of the environment’s dynamics.
2. Temporal-Difference Learning
⏳ 4 hours
Explore TD learning, which combines aspects of Monte Carlo and Dynamic Programming methods, allowing for learning from incomplete episodes.
3. TD Control Methods
⏳ 4 hours
Delve into control methods like Sarsa, Expected Sarsa, and Q-learning, understanding their applications and differences
4. Planning and Learning with Tabular Methods
⏳ 4 hours
Investigate how to integrate planning and learning using the Dyna architecture, which combines model-based and model-free approaches.
5. Final Project
⏳ 6 hours
Apply the concepts learned to implement and analyze reinforcement learning algorithms in practical scenarios.
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Job Outlook
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Prepares learners for roles such as Machine Learning Engineer, AI Researcher, and Data Scientist.
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Applicable in industries like robotics, gaming, finance, and autonomous systems.
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Enhances employability by providing practical skills in reinforcement learning and decision-making algorithms.
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Supports career advancement in fields requiring expertise in adaptive systems and intelligent agents.
