Shanuka B. Herath·Nov 1Can a Distance Heuristic Make Q-Learning Learn Faster? Building and Evaluating a Grid-World AgentAn experimental study of tabular reinforcement learning, potential-based reward shaping, and the challenges of learning optimal paths in…
Shafa·Oct 14Q-Learning vs Deep Q-Learning: Why More Complex Doesn’t Always Mean Better: Explained using Flappy..When we learn about machine learning, it’s easy to assume that more advanced algorithms will always produce better results. A deep…
Irfanon·Oct 7Does Model-Free Really Mean System 1?While studying reinforcement learning, I ran into a connection that initially seemed almost obvious:
Nikhil Gupta·Sep 14I Built Q-Learning, Now I Understand Reinforcement LearningWhat happened when I stopped reading about Reinforcement Learning and actually made an agent learn
InData Reply IT | DataTechbyGiorgia Faccanoni·Sep 2Reinforcement learning for dynamic systems: application of Fitted Q-Iteration methods Introduction
Ujang Riswanto·Aug 21A Deep Dive into Minimax-Q Learning and Its Applications in Multi-Agent EnvironmentsHow AI learns to make smarter decisions when the other side is actively trying to beat it.
Hebsibanisha·Aug 20Difference between Q-learning and Deep Q Networks (DQN).Both Q-learning and Deep Q-Networks (DQN) are reinforcement learning algorithms that learn which action is best to take in a particular…
Ujang Riswanto·Aug 20Mastering Minimax-Q Learning: A Step-by-Step Guide to Game-Theoretic AIHow AI learns to make smarter decisions when the other side is trying just as hard to win.
Ujang Riswanto·Aug 19Minimax-Q Learning: A New Approach to Solving Multi-Agent Decision ProblemsLearn how Minimax-Q Learning combines reinforcement learning and game theory to help AI agents make robust decisions in competitive…
