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   1363

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. 

TensorFlow 2.0 to be Released

New major release of the machine learning platform brought a lot of updates and changes, some stuff even got cut
01 October 2019   217

A significant release of the TensorFlow 2.0 machine learning platform is presented, which provides ready-made implementations of various deep machine learning algorithms, a simple programming interface for building models in Python, and a low-level interface for C ++ that allows you to control the construction and execution of computational graphs. The system code is written in C ++ and Python and is distributed under the Apache license.

The platform was originally developed by the Google Brain team and is used in Google services for speech recognition, facial recognition in photographs, determining the similarity of images, filtering spam in Gmail, selecting news in Google News and organizing the translation taking into account the meaning. Distributed machine learning systems can be created on standard equipment, thanks to the built-in support in TensorFlow for spreading computing to multiple CPUs or GPUs.

TensorFlow provides a library of off-the-shelf numerical computation algorithms implemented through data flow graphs. The nodes in such graphs implement mathematical operations or entry / exit points, while the edges of the graph represent multidimensional data arrays (tensors) that flow between the nodes. The nodes can be assigned to computing devices and run asynchronously, simultaneously processing all the suitable tensors at the same time, which allows you to organize the simultaneous operation of nodes in the neural network by analogy with the simultaneous activation of neurons in the brain.

Get more info about the update at official website.