Enroll Course: https://www.coursera.org/learn/sample-based-learning-methods
The ‘Sample-based Learning Methods’ course offered on Coursera by the University of Alberta is an excellent resource for anyone interested in deepening their understanding of reinforcement learning. This course provides a thorough overview of algorithms that learn near optimal policies through trial and error interactions with the environment, emphasizing learning from the agent’s own experiences without prior knowledge of the environment’s dynamics.
The course is structured into well-organized modules starting with Monte Carlo methods for prediction and control, which teach learners how to estimate value functions using sampled experiences — a crucial concept in reinforcement learning. It then delves into temporal difference (TD) learning, a fundamental technique that combines the strengths of Monte Carlo methods and dynamic programming, making the learning process more efficient and online.
One of the highlights of this course is its comprehensive coverage of TD methods for control, including Sarsa, Q-learning, and Expected Sarsa, along with practical implementation exercises in simulated environments like Cliff World. The course also explores advanced topics like the Dyna architecture, which unifies planning, learning, and acting by estimating models from data and generating hypothetical experiences to boost learning efficiency.
The instructors provide clear explanations, supplemented with practical coding assignments that reinforce the concepts learned. Whether you’re a student, researcher, or professional looking to expand your knowledge in reinforcement learning, this course offers valuable insights and hands-on experience.
I highly recommend this course for its clear structure, practical approach, and in-depth coverage of key algorithms. It is an ideal stepping stone for those aiming to master reinforcement learning techniques and apply them in real-world scenarios.
Enroll Course: https://www.coursera.org/learn/sample-based-learning-methods