This project provides optimized infrastructure for reinforcement learning. It extends the OpenAI gym interface to multiple parallel environments and allows agents to be implemented in TensorFlow and perform batched computation. As a starting point, we provide BatchPPO, an optimized implementation of Proximal Policy Optimization. The algorithm to use is defined in the configuration and pendulum started here uses the included PPO implementation. Check out more pre-defined configurations in agents/scripts/configs.py.

Tech companies are asking to step into doctors' offices with us, and eavesdrop on all the symptoms and concerns we share with our GPs. While doctors and other medical staff are bound by confidentiality and ethics, we haven't yet figured out what it means when a digital third party -- the apps and algorithms -- are allowed in the room, too. Healthcare isn't the place to mimic Facebook's former motto to "move fast and break things", or push regulations to see where they bend, a la Uber. Instead, patients need to trust who's in the consultation room with them, says Nathan Lea, senior research associate at UCL's Institute of Health Informatics and the Farr Institute of Health Informatics Research. "You want the individual to be able to share with the doctor or clinical team as much detail as necessary without the anxiety that someone else will be looking at it," he says.

Elon Musk has issued a warning as Russian president Vladimir Putin claims the nation which leads in AI "will become the ruler of the world." Musk, co-chairman of OpenAI, has long warned of dire consequences for mishandling AI development. OpenAI itself is a non-profit research company that aims to champion promoting and developing friendly AI in a way to benefit humanity. As with any major technology advancement, however, there will undoubtedly be those which aim to weaponise it and to do so before rivals. Based on Putin's comments to Russia-based publication RT, it sounds as if the nation is among them.

Theories of reinforcement learning in neuroscience have focused on two families of algorithms. Model-based algorithms achieve flexibility at computational expense, by rebuilding values from a model of the environment. We examine an intermediate class of algorithms, the successor representation (SR), which caches long-run state expectancies, blending model-free efficiency with model-based flexibility. Although previous reward revaluation studies distinguish model-free from model-based learning algorithms, such designs cannot discriminate between model-based and SR-based algorithms, both of which predict sensitivity to reward revaluation. However, changing the transition structure ('transition revaluation') should selectively impair revaluation for the SR.

Researchers have started developing artificial intelligence with imagination – AI that can reason through decisions and make plans for the future, without being bound by human instructions. Another way to put it would be imagining the consequences of actions before taking them, something we take for granted but which is much harder for robots to do. The team working at Google-owned lab DeepMind says this ability is going to be crucial in developing AI algorithms for the future, allowing systems to better adapt to changing conditions that they haven't been specifically programmed for. "When placing a glass on the edge of a table, for example, we will likely pause to consider how stable it is and whether it might fall," explain the researchers in a blog post. "On the basis of that imagined consequence we might readjust the glass to prevent it from falling and breaking." "If our algorithms are to develop equally sophisticated behaviours, they too must have the capability to'imagine' and reason about the future.

Elon Musk's artificial intelligence research company, OpenAI, is developing a self-learning bot for one of the most complex esports titles: 'Dota 2.' It has already become the ultimate challenge for players, but for top esports pros, it is also a major opportunity. Snoop Dogg and Martha Stewart reenact that famous'Ghost' scene and things get steamy

Can we learn something from the AI playing against Arteezy? I also give a conclusion about what the bot is going to be doing in the future and how OpenAI will try to achieve this.

To learn how humans and AI systems can best live together, we may need to kill a whole lot of Zerg. DeepMind, the AI-focused unit of Alphabet, and the games company Blizzard Entertainment are releasing a set of tools that will let will programmers unleash all sorts of AI algorithms inside the space-themed game StarCraft. The game is more challenging than most of those tackled by AI programs to date. Not only is StarCraft extremely complex, it also requires planning far ahead and trying to second-guess what your opponent is up to. This means developing AI programs capable of matching humans ought to help researchers explore new facets of humanlike intelligence with machines.

Last week was the high point of the Dota 2 competitive year: it was the week of The International, Valve's biggest tournament. On Saturday, Team Liquid walked away with more than $10 million after defeating Newbee 3-0 in the grand final. Right now, one of the requirements to be a good Dota 2 player is that you've got to be a living, breathing human. The game does include some basic computer-controlled bots to practice against, but any seasoned player of the game should have no trouble prevailing over these bots, even on their hardest "Unfair" difficulty (though the Unfair Viper bot is a legendary jerk that's utterly miserable to play against). Last Friday, however, we got a hint of a new, altogether more threatening kind of computer-controlled player: an AI-controlled bot built by Elon Musk's OpenAI.

A new artificial intelligence system teaches itself to recognize a range of visual and audio concepts by watching short video clips. Researchers at Google's DeepMind unit have developed an artificial intelligence (AI) system that teaches itself to recognize a range of visual and audio concepts by watching short video clips. For example, the new system can understand the concept of lawn mowing, even when it has not learned the words to describe what it is hearing or seeing. "We want to build machines that continuously learn about their environment in an autonomous manner," says University of California, Berkeley researcher Pulkit Agrawal. He notes the DeepMind project brings the field one step closer to the goal of creating AI that can teach itself by watching and listening to the world around it.