Dynamic DQN: Neural Architecture Search Revolutionizes Reinforcement Learning
For years, reinforcement learning (RL) has promised to unlock incredible advancements in everything from robotics to game playing – but building effective RL agents often feels like a frustrating guessing game. Manually designing optimal neural networks for these agents is a time-consuming and computationally expensive process, frequently requiring expert intuition and countless iterations that…










