Vishal Ravichandran·Sep 11A Practical Introduction to the Multi-Armed Bandit ProblemUnderstanding exploration, exploitation, and action-selection strategies through a simple bandit problem
Émilia Lilith-Lolita Hoarfrost·Jun 22CODE #3: EMERGENT DECAYING EPSILON-GREEDY Q-LEARNING (PYTHON)Exploring three stages of Q-learning, a classical machine learning concept: epsilon-constant, decaying epsilon-greedy, and emergent…
Promit Haldar·Mar 21Multi-Armed Bandits, Explained: The Epsilon-Greedy Algorithm from First PrinciplesA Deep Dive into Multi-Armed Bandit Testing
darkmatter-rf2j·Dec 31, 2025Exploration is a Philosophical Problem, More Than a Technical One…Ever heard of the idea of a parallel universe? In movies and shows, these are supposed timelines that show the consequences of choosing a…
AmeerSaleem·May 25, 2025Multi Armed Bandits: a stochastic introduction to reinforcement learningAgent-environment interactions, exploration vs exploitation and the epsilon-greedy algorithm for playing with Gaussian bandits.
saurabh kumar pandey·Mar 29, 2025The Art of Exploration with Reinforcement Learning — K Arm Bandit — Part 1The Alien Slot Machine Mystery
Jas Kolb·Jan 29, 2025I wrote a article on “Why UX Must Embrace AI Optimization to Avoid Becoming Obsolete.”“How do we evaluate and fix bias in AI-generated insights to ensure they align with ethical and user-focused design goals?”
David de Villiers·Sep 29, 2024Reinforcement Learning I: Multi-Armed BanditsThis article addresses the challenge of solving multi-armed bandit problems using action-value methods, an important concept in…
InOperations Research BitbyVarun Tyagi·Sep 14, 2024Expediting Decision-Making in Mobile Gaming: A/B Testing Meets Machine LearningReward structures, game mechanics, and user engagement strategies must evolve continually, driven by data, to retain players and keep them…
