DeepMind to Teach AI to Play Quake 3 Like a Human

The authors chose "Flag Capture" for AI agents to learn the mechanics of the game in a procedurally-generated world
06 July 2018   862

Researchers from DeepMind, a division of Alphabet in the field of studying AI, reported about a new development designed to teach AI better to play video games. This time, the experts modified Quake III Arena and its "Capture Flag" mode and forced AIs to learn this game.

Mastering the strategy, tactical understanding, and team play involved in multiplayer video games represents a critical challenge for AI research. Now, through new developments in reinforcement learning, our agents have achieved human-level performance in Quake III Arena Capture the Flag, a complex multi-agent environment and one of the canonical 3D first-person multiplayer games. These agents demonstrate the ability to team up with both artificial agents and human players.
 

DeepMind Team

The authors chose "Flag Capture" for AI agents to learn the mechanics of the game in a procedurally-generated world. Agents played both alone and gathering in teams, including with people. In addition, AI has learned to use tactics such as protecting the base, waiting for the enemy and following the partner:

AI Behaviour
AI Behaviour

The developers used the training method with reinforcement, and the AI ​​did not receive any additional information, except for the picture on the screen. The team of agents trained with each match, receiving a positive response when winning. At the same time, each of them had its own internal reward. AI is based on a pair of recurrent neural networks, fast and slow, each of which studies the transition from scoring points to an internal reward.

According to the results of the study, the authors found that AI agents not only won more often than people, but also were more united. According to the received data, the AI ​​coefficient Elo, responsible for the chance of winning, is higher than the human:

Performance Graph
Performance Graph

The authors claim that in the future they will develop technologies of simultaneous training with the reinforcement of several AI agents, and will also pay more attention to uniting agents and people in teams for greater efficiency. 

Neural Network to Create Landscapes from Sketches

Nvidia created GauGAN model that uses generative-competitive neural networks to process segmented images and create beautiful landscapes from peoples' sketches
20 March 2019   150

At the GTC 2019 conference, NVIDIA presented a demo version of the GauGAN neural network, which can turn sketchy drawings into photorealistic images.

The GauGAN model, named after the famous artist Paul Gauguin, uses generative-competitive neural networks to process segmented images. The generator creates an image and transfers it to the discriminator trained in real photographs. He in turn pixel-by-pixel tells the generator what to fix and where.

Simply put, the principle of the neural network is similar to the coloring of the coloring, but instead of children's drawings, it produces beautiful landscapes. Its creators emphasize that it does not just glue pieces of images, but generates unique ones, like a real artist.

Among other things, the neural network is able to imitate the styles of various artists and change the times of the day and year in the image. It also generates realistic reflections on water surfaces, such as ponds and rivers.

So far, GauGAN is configured to work with landscapes, but the neural network architecture allows us to train it to create urban images as well. The source text of the report in PDF is available here.

GauGAN can be useful to both architects and city planners, and landscape designers with game developers. An AI that understands what the real world looks like will simplify the implementation of their ideas and help you quickly change them. Soon the neural network will be available on the AI ​​Playground.