What will you learn in this Sample-based Learning Methods Course
Understand Temporal-Difference (TD) learning and Monte Carlo methods as strategies for estimating value functions from sampled experience.
Implement and apply TD algorithms, including Expected Sarsa and Q-learning, for control tasks.
Differentiate between on-policy and off-policy control methods.
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.
Get certificate
Job Outlook
Prepares learners for roles such as Machine Learning Engineer, AI Researcher, and Data Scientist.
Applicable in industries like robotics, gaming, finance, and autonomous systems.
Enhances employability by providing practical skills in reinforcement learning and decision-making algorithms.
Supports career advancement in fields requiring expertise in adaptive systems and intelligent agents.
Specification: Sample-based Learning Methods
|
