The long-term goal of artificial intelligence is to solve advanced real-world challenges. Games have served as stepping stones along this path for decades, from Backgammon (1992) to Chess (1997) to Atari (2013)[1-3]. In 2016, AlphaGo defeated the world champion at Go using deep reinforcement learning and Monte Carlo tree search[4]. In recent years, reinforcement learning (RL) models have tackled tasks as varied as robotic manipulation[5], text summarization [6], and video games such as Starcraft[7] and Minecraft[8]. Relative to previous AI milestones like Chess or Go, complex video games start to capture the complexity and continuous nature of the real world. Dota 2 is a multiplayer real-time strategy game produced by Valve Corporation in 2013, which averaged between 500,000 and 1,000,000 concurrent players between 2013 and 2019. The game is actively played by full time professionals; the prize pool for the 2019 international championship exceeded $35 million (the largest of any esports game in the world)[9, 10]. The game presents challenges for reinforcement learning due to long time horizons, partial observability, and high dimensionality of observation and action spaces.

We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: Our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes.