Ok, I know a bit about dota (been playing it for 8 years now). I will try my best to put this into perspective. What: It beat players that many considered to be the absolute best at dota. The environment: 2 players move along a lane with the goal of destroying the other's defensive structure or killing the player 2 times for victory. Every 30 seconds weak npc minions enter the lane attack each other and players.

Last year, Google's DeepMind announced a partnership with Blizzard Entertainment to develop and test artificial intelligence (AI) agents in the popular real-time strategy game StarCraft II. Now, DeepMind has released a series of tools they're calling StarCraft II Learning Environment (SC2LE) to test their agents against human competitors, as well as enable researchers to develop their own agents for the game. "Testing our agents in games that are not specifically designed for AI research, and where humans play well, is crucial to benchmark agent performance," DeepMind's team wrote in a blog post. The large pool of online StarCraft II players will provide a huge variety of "extremely talented opponents" from which the AI can learn. Details of DeepMind's research were published in a paper alongside the released toolset, which includes a machine learning API; a dataset of game replays; an open source version of PySC2, the Python component SC2LE; and more.

Tic-tac-toe, checkers, chess, Go, poker. Artificial intelligence rolled over each of these games like a relentless tide. No one expects the robot to win anytime soon. But when it does, it will be a far greater achievement than DeepMind's conquest of Go--and not just because StarCraft is a professional e-sport watched by fans for millions of hours each month. DeepMind and Blizzard Entertainment, the company behind StarCraft, just released the tools to let AI researchers create bots capable of competing in a galactic war against humans.

StarCraft and StarCraft II are among the biggest and most successful games of all time, with players competing in tournaments for more than 20 years. The original game is also already used by AI and ML researchers, who compete annually in the AIIDE bot competition. Part of StarCraft's longevity is down to the rich, multi-layered gameplay, which also makes it an ideal environment for AI research. For example, while the objective of the game is to beat the opponent, the player must also carry out and balance a number of sub-goals, such as gathering resources or building structures. In addition, a game can take from a few minutes to one hour to complete, meaning actions taken early in the game may not pay-off for a long time.

Google's DeepMind AI has mastered Atari arcade classics and beaten human world champions at board games, and now it's set to take on a much bigger challenge - StarCraft II. The research lab has teamed up with video game company Blizzard Entertainment to open StarCraft II as an AI research environment the firms hope will give insight into the most complex problems related to artificial intelligence. Together, they are releasing a set of tools to accelerate AI research in the strategy game their algorithm can eventually beat it. Google's DeepMind research lab has teamed up with video game company Blizzard Entertainment to open StarCraft II as an AI research environment the firms hope will give insight into the most complex problems related to artificial intelligence DeepMind has tackled games like Atari Breakout, but StarCraft II presents new challenges in how it contains multiple layers and sub-goals. Players must accomplish smaller goals along the way, such as gathering resources or building structures.

To an untrained AI, the world is a blur of confusing data streams. Most humans have no problem making sense of the sights and sounds around them, but algorithms tend only to acquire this skill if those sights and sounds are explicitly labelled for them. Now DeepMind has developed an AI that teaches itself to recognise a range of visual and audio concepts just by watching tiny snippets of video. This AI can grasp the concept of lawn mowing or tickling, for example, but it hasn't been taught the words to describe what it's hearing or seeing. "We want to build machines that continuously learn about their environment in an autonomous manner," says Pulkit Agrawal at the University of California, Berkeley.

Google, apparently tired of trouncing human players at Go with its DeepMind AI, set its computer intelligence up with Blizzard's video game Starcraft II last fall. It seems that was more than a stunt: Today, Google announced it has built a whole research environment around training its AI to play the space-age strategy game -- and it's publicly available. Anyone who wants can tinker with DeepMind's new toolset, SC2LE, to facilitate their own AI research. The bundle includes a pair of kits up on GitHub: first, Blizzard's machine learning API, which has Linux tools for the first time, and then an open source version of the DeepMind toolset called PySC2. Blizzard also provided some extra goodies, like a dataset of anonymized 1v1 game replays for programmers to parse through, along with sample code and some sample bots.

Two Minute Papers reviews how DeepMind learned how to play the Atari classic "Break Out" simply by observing the game being played from a video feed. The original paper "Imagination-Augmented Agents for Deep Reinforcement Learning" is online at https://arxiv.org/abs/1707.06203

Starcraft II has been a target for Alphabet's DeepMind AI research for a while now – the UK AI company took on Blizzard's sci-fi strategy game starting last year, and announced plans to create an open AI research environment based on the game to make it possible for others to contribute to the effort of creating a virtual agent who can best the top human StarCraft players in the world. Now, DeepMind and Blizzard are opening the doors to that environment, with new tools including a machine learning API, a large game replay dataset, an open source DeepMind toolset and more. The new release of the StarCraft II API on the Blizzard side includes a Linux package made to be able to run in the cloud, as well as support for Windows and Mac. It also has support for offline AI vs. AI matches, and those anonymized game replays from actual human players for training up agents, which is starting out at 65,000 complete matches, and will grow to over 500,000 over the course of the next few weeks. StarCraft II is such a useful environment for AI research basically because of how complex and varied the games can be, with multiple open routes to victory for each individual match.

Novice here: I really want to try this Starcraft API but I don't know how to start. I believe this uses more reinforcement learning and agent-based models (which honestly I am not familiar with yet) What are good papers to get started on this?