Resource of free step by step video how to guides to get you started with machine learning.
Tuesday, August 4, 2020
PCGRL: Procedural Content Generation via Reinforcement Learning (Paper Explained)
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://ift.tt/2GrzqLJ Code: https://ift.tt/2S1zmYu 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 Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n
Subscribe to:
Post Comments (Atom)
-
JavaやC++で作成された具体的なルールに従って動く従来のプログラムと違い、機械学習はデータからルール自体を推測するシステムです。機械学習は具体的にどのようなコードで構成されているでしょうか? 機械学習ゼロからヒーローへの第一部ではそのような疑問に応えるため、ガイドのチャー...
-
Using GPUs in TensorFlow, TensorBoard in notebooks, finding new datasets, & more! (#AskTensorFlow) [Collection] In a special live ep...
-
#minecraft #neuralnetwork #backpropagation I built an analog neural network in vanilla Minecraft without any mods or command blocks. The n...
-
Using More Data - Deep Learning with Neural Networks and TensorFlow part 8 [Collection] Welcome to part eight of the Deep Learning with ...
-
Linear Algebra Tutorial on the Determinant of a Matrix 🤖Welcome to our Linear Algebra for AI tutorial! This tutorial is designed for both...
-
STUMPY is a robust and scalable Python library for computing a matrix profile, which can create valuable insights about our time series. STU...
-
❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers 📝 The paper "Alias-Free GAN" is available here: h...
-
Why are humans so good at video games? Maybe it's because a lot of games are designed with humans in mind. What happens if we change t...
-
Visual scenes are often comprised of sets of independent objects. Yet, current vision models make no assumptions about the nature of the p...
-
#ai #attention #transformer #deeplearning Transformers are famous for two things: Their superior performance and their insane requirements...
No comments:
Post a Comment