Post on 23-Jan-2018
Deep Learning
for
Game Development
Magnus NordinTechnical Director, EA SEED
Future Worlds
Deep LearningVirtual Humans
Prototypes
f( ) = cat
f( ) =
f( ) =
”A person flying a kite
on a beach”
”A coffee, please.”
f( ) =”A coffee, please.”
f( ) =
f( ) =This bird is red and brown in
color, with a stubby beak
f( ) =This flower is pink, white,
and yellow in color, and
has petals that are striped
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks, Zhang et al, 2016
f( ) =
Reinforcement Learning for
Game AI
1987 2015
Reinforcement Learning
Observations/Rewards
AgentEnvironment
Goal
Actions
Learning by doing
Bad Balls
Our hero
Eat! +1 point
Avoid! -5 points
EA Trailer
Single-Action vs Multi-Action
Objective area
The agent
Health
The opposition
Supplies
50M steps of training
100M steps of training
Generalization
Challenges
Observation (vision vs state)
Combining with classical game AI
Giving designers controlGoals
Imitation
Execution (GPUs are typically busy with graphics ☺)
Machine Learning Gaming Use Cases
Physics
Physics Forests: Real-time Fluid Simulation using Machine Learning, Ladicky et al., 2015, www.physicsforests.com
GAN geometry
Interactive Example Based Terrain Authoring with Conditional Adversarial Networks, Guérin et al, 2017
Representation Learning and Adversarial Generation of 3D Point Clouds, Achlioptas et al., 2017
• Needs to be Full of Life (Spore connection?)
Emergence of Locomotion Behaviours in Rich Environments, Heess et al., 2017
El último vals
A Neural Parametric Singing Synthesizer, Blaauw & Bonada, 2017
Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion, Karras et al., 2017, NVIDIA
The Future
Geek & Sundry, D&Diesel
[clip]
• Live role playing
MMORPG
MMO(RPG)s
• Not much RPG [pic of WoW raid]
• hence ”MMOs”
True Role Playing
f( ) =
f( ) =
Sopranos © HBO 2006
Games will change more in the next 5 years than they have in the last 45 years
Magnus NordinTechnical Director, EA SEED