DeepGamingAI's video: This AI Learns To Design Game Maps Game Futurology 1

This is the episode of the video series "Game Futurology" covering the paper "PCGRL: Procedural Content Generation via Reinforcement Learning" by Ahmed Khalifa, Philip Bontrager, Sam Earle and Julian Togelius. PDF: https://arxiv.org/pdf/2001.09212.pdf Code: https://github.com/amidos2006/gym-pcgrl Game Futurology: This is a video series consisting of short 2-3 minute overview of research papers in the field of AI and Game Development. This series aims to ponder over what the future games might look like based on the latest academic research going on in the field today. Subscribe for more weekly videos! Abstract: We investigate how reinforcement learning can be used to train level-designing agents. This represents a new approach to procedural content generation in games, where level design is framed as a game, and the content generator itself is learned. By seeing the design problem as a sequential task, we can use reinforcement learning to learn how to take the next action so that the expected final level quality is maximized. This approach can be used when few or no examples exist to train from, and the trained generator is very fast. We investigate three different ways of transforming two dimensional level design problems into Markov decision processes, and apply these to three game environments. Music Credits: https://www.fesliyanstudios.com/ ---------------------------------------------------------------- • YouTube - https://www.youtube.com/c/DeepGamingA... • Twitter - https://twitter.com/deepgamingai • Medium - https://medium.com/@chintan.t93 • GitHub - https://github.com/ChintanTrivedi --------------------------------------------------------------------