FPS has much higher success rate than uniform sampling: 0.4 vs 0.15 at

This article is part of our coverage of the latest in AI research. Reinforcement learning is one of the fascinating fields of computer science, and it has proven useful in solving some of the toughest challenges of artificial intelligence and robotics. Some scientists believe that reinforcement learning will play a key role in cracking the enigma of human-level artificial intelligence. But many hurdles stand between current reinforcement learning systems and a possible path toward more general and robust forms of AI. Many RL systems struggle with long-term planning, training-sample efficiency, transferring knowledge to new tasks, dealing with the inconsistencies of input signals and rewards, and other challenges that occur in real-world applications.

Deep Learning methods are replacing traditional software methods in solving real-world problems. Cheap and easily available computational power combined with labeled big datasets enabled deep learning algorithms to show their full potential. AlexNet paper(2012; Krizhevsky et al.[9]) showed feeding sufficient data into deep neural networks successfully learned to extract representations better than handcrafted features which let the start an era known as the rise of Deep Learning. Their great success in solving otherwise hard engineering problems such as object detection, voice recognition, chatbots, robotic manipulation and autonomous systems shown they can be applied to various fields thanks to their generalisation capability.[16] Path Planning(Motion Planning) is defined as computing a continuous path from starting position S to destination position D while avoiding any known obstacles in the way.[20] Whether it is in 2D or 3D geometry, any robotic system then will able to follow the computed path to reach it's destination. Real World robotic systems tend to use more explainable and reproducible algorithms based on interval based search (A star or Dijkstra) or sampling-based algorithms. We wanted to show a reward based algorithm that depends on Markov Decision Process(MDP) by trying to maximize cumulative future rewards can also complete long term path planning tasks. Advantage of using this option will allow autonomous robot(in our case simulated quadrotor) to create paths in non holonomic constraints which is something current methods fails to achieve.[1][17]

Planning has been long considered one of the cognitive abilities of the human mind that is nearly impossible to replicate by artificial intelligence(AI). Some neuroscientists even relate to future planning as one of the key characteristics of human consciousness. Planning does not only requires understanding a specific objective but also projecting that objective onto an environment whose characteristics are unknown in the present. Humans are able to plan not only because we are able to understand a specific task in detail but because our ability to understand our surrounding environment enough that we can project the outcome of that task in the future. In the context of AI, reinforcement learning is the discipline that has been trying to build long-term planning capabilities in AI agents.

Airlines are looking to artificial intelligence to turn age-old processes upside down and make flying more efficent. While the industry has traditionally relied on long-term planning across maintenance, schedules, crew and other areas, AI could mean a move to flexible processes. Lufthansa Group chief digital officer Christian Langer believes AI could spell the end of planning. "Airlines have an inflexible asset base - aircraft, hangars, spare parts - it's expensive and inflexible. Then, we have a very flexible customer base, so it's how do you mitigate that? He points to the long-term planning of crew and summer schedules, adding that airlines also typically plan for groups of things such as customer segments and part numbers. "With upcoming data, computational power and finally AI, it is going to shift from long-term to real-time and from groups of things to individuals, and this turns all the planning systems upside down." Speaking during last week's Aviation Festival in London, Langer says small companies were tackling parts of the equation such as Hopper and Flyr for price predictions and Dohop and Kiwi for the network side of things. "We're trying to rethink and rebuild all the planning systems we have to find out what would be the real-time, individual alternative.

Last week, humanity struck back against the machines -- sort of. Actually, we beat them at a video game. In a best-of-three match, two teams of pro gamers overcame a squad of AI bots that were created by the Elon Musk-founded research lab OpenAI. The competitors were playing Dota 2, a phenomenally popular and complex battle arena game. But the match was also something of a litmus test for artificial intelligence: the latest high-profile measure of our ambition to create machines that can out-think us. In the human-AI scorecard, artificial intelligence has racked up some big wins recently.

Beating humans at board games is passé in the AI world. Now, top academics and tech companies want to challenge us at video games instead. Today, OpenAI, a research lab founded by Elon Musk and Sam Altman, announced its latest milestone: a team of AI agents that can beat the top 1 percent of amateurs at popular battle arena game Dota 2. You may remember that OpenAI first strode into the world of Dota 2 last August, unveiling a system that could beat the top players at 1v1 matches. However, this game type greatly reduces the challenge of Dota 2. OpenAI has now upgraded its bots to play humans in 5v5 match-ups, which require more coordination and long-term planning. And while OpenAI has yet to challenge the game's very best players, it will do so later this year at The International, a Dota 2 tournament that's the biggest annual event on the e-sports calendar.

Ask your friends why they go to Vegas, and I'm sure you won't get the same answer twice. Casually surveying a few colleagues and friends yielded "the food and nightlife," "bachelor party weekend," "catching the Jerry Seinfeld show," "Calvin Harris is DJing," "CES conference," and…"rock climbing." What could these wide-ranging answers possibly have in common? Product development requires mid-term to long-term planning. It takes a long time to change a casino, experiment, and get a read on reception to those changes. Since the Great Recession, Las Vegas has come to Jesus.

Artificial intelligence has come a long way in the 20 years since International Business Machines Corp.'s Deep Blue beat world champion Garry Kasparov in a six-game chess match, or even the six years since Watson trounced Ken Jennings on Jeopardy! Computers have beaten top human players at checkers, backgammon, poker, and go. Add to the list Super Smash Bros. Melee, a 2001 Nintendo Co. fighting game that lets you pit, say, Mario against Pikachu. Humanity has MIT researchers to thank for this defeat, chronicled in a paper they published in February, but Melee isn't the only video game getting a lot of playtime from learning machines. AI software has cracked Super Mario Bros.; early Atari SA games such as Space Invaders; arcade mainstays Pac-Man and Mortal Kombat; even mobile favorite Angry Birds.