5 posts with this topic
A deep dive into Temporal Difference (TD) Learning for Reinforcement Learning, an idea that forms basis for modern deep reinforcement learning algorithms
A deep dive into Monte Carlo methods for Reinforcement Learning, focusing on learning optimal strategies from raw experience without a model.
Introduction to Dynamic Programming: Policy Evaluation, Policy Iteration, Value Iteration and Generalized Policy Iteration
Understanding the multi-armed bandit problem and how to solve it using reinforcement learning alongwith interactive demo. Chapter 3 of Reinforcement Learning: An Introduction by Sutton and Barto
Understanding how different algorithms plays Tic-Tac-Toe and why RL wins at it. Interactive demo included!
