KAKANAKOU Miguel·May 25Teaching a Neural Network to Play Breakout: A Full DQN Implementation in JAXAfter around 9 hours of training, my DQN agent was playing Breakout with an average score of 42.52 and a peak of 90. Not…
Saqib Raza·Mar 27From Q-Values to Deep Q-Networks: A Practical Guide to Value-Based Reinforcement LearningUnderstanding V(s), Q(s,a), Bellman equations, TD learning, Monte Carlo methods, and how DQN bridges classical RL with deep learning.
Lucapiai·Mar 22Winning At Snake With Reinforcement LearningHow dynamically switching between distinct Deep Q-Network agents overcomes the limitations of a single reinforcement learning model.
FlipGoms·Jan 24AI Car Racing: Teach AI to DriveTeaching a DQN agent to master the track in Gymnasium’s CarRacing using Python. Reinforcement learning meets the fast lane!
chloehyc·Jan 12DDQN vs DQN: Who Balances Better?Reinforcement Learning: Learning through trial and error
Anika Ip·Jan 12A Practical Guide to Double DQN: Reducing Overestimation in Deep Reinforcement LearningReinforcement learning, also known as RL, is a method to train an agent to make sequential decisions. This is done through allowing the…
Mohit Wani·Dec 25, 2025Deep Dive: DQN vs Double DQN Conquering Overestimation in Deep RLWhen DeepMind’s Deep Q-Network (DQN) successfully learned to play Atari games from raw pixels in 2013, it marked a watershed moment in…
Christian Bernecker·Nov 6, 2025From Q-Tables to Neural Networks: An Introduction to Deep Q-Networks (DQN)Escaping the “Curse of Dimensionality”: Why Q-Tables Fail and How Neural Networks Save the Day
Abhilash Hathwar·Oct 25, 2025When the Smartest Agent Fails: Lessons from Rainbow DQNSince DeepMind’s DQN mastered Atari games, reinforcement learning has evolved, from Double DQNs to comprehensive Rainbow DQN methods. Each…
