DeepMind’s XLearn trains AI agents to complete complex tasks

DeepMind today detailed its latest efforts to create AI systems capable of completing a range of different, unique tasks. By designing a virtual environment called XLand, the Alphabet-backed lab says that it managed to train systems with the ability to succeed at problems and games including hide and seek, capture the flag, and finding objects, some of which they didn’t encounter during training.

The AI technique known as reinforcement learning has shown remarkable potential, enabling systems to learn to play games like chess, shogi, Go, and StarCraft II through a repetitive process of trial and error. But a lack of training data has been one of the major factors limiting reinforcement learning–trained systems’ behavior being general enough to apply across diverse games. Without being able to train systems on a vast enough set of tasks, systems trained with reinforcement learning have been unable to adapt their learned behaviors to new tasks.

DeepMind designed XLand to address this, which includes multiplayer games within consistent, “human-relatable” digital worlds. The simulated space allows for procedurally generated tasks, enabling systems to train on — and generate experience from — tasks that are created programmatically.

XLand offers billions of tasks across varied worlds and players. AI controls players in an environment meant to simulate the physical world, training on a number of cooperative and competitive games. Each player’s objective is to maximize rewards, and each game defines the individual rewards for the players.

“These complex, non-linear interactions create an ideal source of data to train on, since sometimes even small changes in the components of the environment can result in large changes in the challenges for the [systems],” DeepMind explains in a blog post.