PCGRL: Procedural Content Generation via Reinforcement Learning (Paper Explained)

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#ai #research #gaming

Deep RL is usually used to solve games, but this paper turns the process on its head and applies RL to game level creation. Compared to traditional approaches, it frames level design as a sequential decision making progress and ends up with a fast and diverse level generator.

OUTLINE:
0:00 - Intro & Overview
1:30 - Level Design via Reinforcement Learning
3:00 - Reinforcement Learning
4:45 - Observation Space
5:40 - Action Space
15:40 - Change Percentage Limit
20:50 - Quantitative Results
22:10 - Conclusion & Outlook

Paper: https://arxiv.org/abs/2001.09212
Code: https://github.com/amidos2006/gym-pcgrl

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.

Authors: Ahmed Khalifa, Philip Bontrager, Sam Earle, Julian Togelius

ERRATA:
- The reward is given after each step.

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Tags:
deep learning
machine learning
arxiv
explained
neural networks
ai
artificial intelligence
paper
rl
reinforcement learning
level design
game design
video game
sobokan
sokoban
zelda
maze
agent
turtle
observation
reward
action
space
deep rl
deep reinforcement learning
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minecraft