Teenager invents robot to solve Rubik's Cube BBCRuarcc the year 10 student who has programmed a robot that can solve a Rubik's Cube puzzle A 13-year-old schoolboy has invented a Lego robot that can solve a Rubik's cube. Ruarcc, from St Malachy's College in north Belfast, first took steps to create puzzle-solving robot prototypes in his second year at school, aged 12. This was made possible after the school launched its creative digital technology hub (CDTH) last year. Teacher Clare McGrath commented she "didn't believe" that Ruarcc's robot would work at first.'People are amazed my robot can solve Rubik's Cube' Ruarcc told BBC News NI it was "frustrating", but he worked on making it better. "People tend to be amazed that it can solve one," he said.

Half a year has passed since my book "Deep Reinforcement Learning Hands-On" has seen the light. It took me almost a year to write the book and after some time of rest from writing I've discovered that explaining RL methods and turning theoretical papers into working code is a lot of fun for me and I don't want to stop. Luckily, RL domain is evolving, so, there are lots of topics to write about. In mass perception, Deep Reinforcement Learning is a tool to be used mostly for game playing. This is not surprising, given the fact, that historically, the first success in the field was achieved in Atari game suite by Deep Mind in 2015. Atari benchmark suite turned out to be very successful for RL problems and, even now, lots of research papers are using it for demonstrating the efficiency of their methods. As the RL field progresses, the classical 53 Atari games continue to become less and less challenging (at the time of writing more than half of games are solved with super-human accuracy) and researches turn to more complex games, like StarCraft and Dota2. But this bias towards games creates a false impression "RL is about playing games'', which is very far from the truth. In my book, published in June 2018, I've tried to counterbalance this by accompanying Atari games with the examples from other domains, including stock trading (chapter 8), chatbots and NLP problems (chapter 12), web navigation automation (chapter 13), continuous control (chapters 14…16) and boards games (chapter 18). In fact RL having very flexible MDP model potentially could be applied to a wide variety of domains, where computer games is just one convenient and spectacular example of the complicated decision making. In this article I've tried to write a detailed description of the recent attempt to apply RL to a field of combinatorial optimisation. The paper discussed was published by the group of researchers from UCI (University of California, Irvine) and called "Solving the Rubik's Cube Without Human Knowledge''.

Colin G. Johnson, an associate professor at the University of Nottingham, recently developed a deep-learning technique that can learn a so-called "fitness function" from a set of sample solutions to a problem. This technique, presented in a paper published in Wiley's Expert Systems journal, was initially trained to solve the Rubik's cube, the popular 3-D combination puzzle invented by Hungarian sculptor Ernő Rubik. "The aim of our paper was to use machine learning to learn to solve the Rubik's cube," Colin G. Johnson, one of the researchers who carried out the study, told TechXplore. "Rubik's cube is a very complex puzzle, but any of the vast number of combinations is at most 20 steps from a solution. So the approach we take here is to try and solve the problem by learning to do each of those steps individually."

However, these AI algorithms cannot explain the thought processes behind their decisions. A computer that masters protein folding and also tells researchers more about the rules of biology is much more useful than a computer that folds proteins without explanation. Therefore, AI researchers like me are now turning our efforts toward developing AI algorithms that can explain themselves in a manner that humans can understand. If we can do this, I believe that AI will be able to uncover and teach people new facts about the world that have not yet been discovered, leading to new innovations. One field of AI, called reinforcement learning, studies how computers can learn from their own experiences.

Last year we were amazed by the level of dexterity achieved by OpenAI's Dactyl system which was able to learn how to manipulate a cube block to display any commanded side/face.If you missed that article, read about it here. OpenAI then set themselves a harder task of teaching the robotic hand to solve a Rubik's cube. Quite a daunting task made no easier by the fact that it would use one hand which most humans would find it hard to do. OpenAI harnessed the power of neural networks which are trained entirely in simulation. However, one of the main challenges faced was to make the simulations as realistic as possible because physical factors like friction, elasticity etc. are very hard to model.

Yesterday, artificial intelligence(AI) powerhouse OpenAI astonished the world by unveiling a prototype of a robotic arm that could solve a Rubik's cube with one hand. The prototype didn't only represent a milestone for the robotics ecosystem in solving high complexity tasks that actively require sensorial information but it also resulted on a major achievement for the AI community. The reason is that the OpenAI robot was completely trained using simulations based on the reinforcement learning models that the OpenAI Five system used to beat human players in Dota2. The research was discussed in a paper that accompanied the news. The importance of OpenAI's achievement was not about designing a robot that could solve a Rubik's cube.

The latest neural network to impress is DeepCubeA from Forest Agostinelli, Stephen McAleer, Alexander Shmakov and Pierre Baldi of the University of California, Irvine. This is a deep neural network that learns a range of combinatorial puzzles - sliding block15, 24, 35, 48 puzzles, Lights Out, Sokoban and, of course, Rubik's cube. The network learns a reinforcment value function, but it does this "backwards". That is, it starts from a solution and randomly takes moves away from the goal. As it steps away from the goal, the moves and configurations become increasingly low in value, - i.e. they are moving away from the goal.

Two algorithms, collectively called Deep Cube, typically can solve the 3-D combination puzzle within 30 moves, which is less than or equal to systems that use human knowledge, according to the research paper. Less than 5.8% of the world's population can solve the Rubik's Cube, according to the Rubik's website.

Deep-learning machines have figured out how to master games like chess or Mortal Kombat. Now, computer scientists at the University of California, Irvine taken things to the third dimension by creating an algorithm that can figure out how to solve a Rubik's Cube, a surprisingly difficult change. "Our algorithm is able to solve 100 percent of randomly scrambled cubes while achieving a median solve length of 30 moves - less than or equal to solvers that employ human domain knowledge," say the scientists in the abstract to their paper, up on Arvix. The algorithm, called DeepCube, uses what's known as "autodidactic iteration," a form of machine learning developed by the authors of the paper. The big challenge of autodidactic iteration was to allow machines to find their own rewards in solving a puzzle, a goal they can reach.

If you're looking to play around with robotics, Lego's Mindstorms EV3 is a great way to get started. So is the ultra-versatile Raspberry Pi. Combining the two to create a Rubik's Cube-solving robot? That sounds like a good time to us! The Lego bricks take care of the physical moves required to solve the puzzle.