DeepMind wants to teach robots to play board games

Mastering physical systems with abstract goals is an unsolved challenge in AI. To encourage the development of techniques that might overcome it, researchers at DeepMind created custom scenarios for the physics engine MuJoCo that task an AI agent with coordinating perception, reasoning, and motor control over time. They believe that the library, which they've made publicly available, can help bridge the gap between abstract planning and embodied control. Recent work in machine learning has led to algorithms capable of mastering board games such as Go, chess, and shogi. These algorithms observe the states of games and control these states directly with their actions, unlike humans, who don't just reason about the moves but look at the board and physically manipulate the game pieces with their fingers.