The space and renewable energy tycoon has launched yet another pioneer non-profit project which can change the world of robotics today. Elon Musk developed a 1 billion-program "Robot Gym" to train artificial intelligence. He says he wanted the robots to be as versatile as humans when it comes to thinking. Our reinforcement learning toolkit, OpenAI Gym, is now in public beta: https://t.co/8fMRLjSJQb. Tesla's CEO, together with Sam Altman, created the artificial intelligence non-profit research company.

Clusters defined over a dataset by unsupervised clustering often present groupings which differ from the expected solution. This is primarily the case when some scarce knowledge of the problem exists beforehand that partially identifies desired characteristics of clusters. However conventional clustering algorithms are not defined to expect any supervision from the external world, as they are supposed to be completely unsupervised. As a result they can not benefit or effectively take into account available information about the use or properties of the clusters. In this paper we propose a reinforcement learning approach to address this problem where existing, unmodified unsupervised clustering algorithms are augmented in a way that the available sparse information is utilized to achieve more appropriate clusters. Our model works with any clustering algorithm, but the input to the algorithm, instead of being the original dataset, is a scaled version of the same, where the scaling factors are determined by the reinforcement learning algorithm.

In reinforcement learning (RL), reward shaping is used to show the desirable behavior by assigning positive or negative reward for learner’s preceding action. However, for reward shaping through human-generated rewards, an important aspect is to make it approachable to humans. Typically, a human teacher’s role requires being watchful of agent’s action to assign judgmental feedback based on prior knowledge. It can be a mentally tough and unpleasant exercise especially for lengthy teaching sessions. We present a method, Shaping from Interactive Demonstrations (SfID), which instead of judgmental reward takes action label from human. Therefore, it simplifies the teacher’s role to demonstrating the action to select from a state. We compare SfID with a standard reward shaping approach on Sokoban domain. The results show the competitiveness of SfID with the standard reward shaping.

'If there is Artificial Intelligence then we won't need to think' said my recently-turned-ten daughter, on our way driving her to a piano lesson. She left me speechless while I was trying, in vain, to remove from my mind a sticky image from the movie'The Hitchhiker guide to the Galaxy'. A supercomputer called'Deep Thought' which, after 7,5 million years of calculations rendered the answer to the Ultimate Question of Life, the Universe and Everything, or, namely, the number 42. Quirky cult movies aside, the impact of artificial intelligence is immense, and the implications phenomenal. Artificial Intelligence (A.I.) is on the hype again. There are dystopian, terminator-like views backed up with warnings from renowned scientists, Stephen Hawking to start with. Even Tesla and SpaceX founder Elon Musk appeared on CNBC's'Closing Bell' cautioning about the potential risks of this technology.

Open AI Gym: What do you think about the Reinforcement Learning toolkit and its evaluation methods? Its been approximately 10 days since the release of Gym. How has your experience with it been so far? Do you think academic/non-academic researchers will embrace it and use it as benchmarks in their publications? Or will it just end up as some highly decorated playground over time? More importantly, do you think it will do anything to accelerate AI research as claimed??

The theory for RL hasn't changed - people were using NNs to train RL models years ago. Deep Learning is just a different kind of NN, the underlying theory for RL still applies.

High-tech entrepreneur Elon Musk has launched an open-source training "gym" for artificial-intelligence programmers. It's an interesting move for a man who in 2014 said artificial intelligence, or A.I., will pose a threat to the human race. "I think we should be very careful about artificial intelligence," Musk said about a year and a half ago during an MIT symposium. "If I were to guess at what our biggest existential threat is, it's probably that... with artificial intelligence, we are summoning the demon. In all those stories with the guy with the pentagram and the holy water, and he's sure he can control the demon. Today, Musk is moving to help programmers use A.I. and machine learning to build smart robots and smart devices. "We're releasing the public beta of OpenAI Gym, a toolkit for developing and comparing reinforcement learning (RL) algorithms," wrote Greg Brockman, OpenAI's CTO, and John Schulman, a scientist working with OpenAI, in a blog post . "We originally built OpenAI Gym as a tool to accelerate our own RL research.

Today OpenAI, a non-profit artificial intelligence research company, launched OpenAI Gym, a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Go. OpenAI researcher John Schulman shared some details about his organization, and how OpenAI Gym will make it easier for AI researchers to design, iterate and improve their next generation applications. John studied physics at Caltech, and went to UC Berkeley for graduate school. There, after a brief stint in neuroscience, he studied machine learning and robotics under Pieter Abbeel, eventually honing in on reinforcement learning as his primary topic of interest.

I posted about this earlier but mistakenly clicked text instead of link and didn't link the article so here it is. We applied Deep Q-learning with a supervised initialization to the board-game hex and obtained promising results. This was somewhat surprising to me as I suspected statistical bias involved in Q-learning may be a major issue with the large space of actions involved in a stone placement game. Code can be found here.