"Reinforcement learning is learning what to do – how to map situations to actions – so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them."
– Sutton, Richard S. and Andrew G. Barto. Reinforcement Learning: An Introduction. (1.1). MIT Press, Cambridge, MA, 1998.
Robots trained with reinforcement learning (RL) have the potential to be used across a huge variety of challenging real world problems. To apply RL to a new problem, you typically set up the environment, define a reward function, and train the robot to solve the task by allowing it to explore the new environment from scratch. While this may eventually work, these "online" RL methods are data hungry and repeating this data inefficient process for every new problem makes it difficult to apply online RL to real world robotics problems. What if instead of repeating the data collection and learning process from scratch every time, we were able to reuse data across multiple problems or experiments? By doing so, we could greatly reduce the burden of data collection with every new problem that is encountered.
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The "Curly" curling robots are capturing hearts around the world. A product of Korea University in Seoul and the Berlin Institute of Technology, the deep reinforcement learning powered bots slide stones along ice in a winter sport that dates to the 16th century. As much as their human-expert-bettering accuracy or technology impresses, a big part of the Curly appeal is how we see the little machines in the physical space: the determined manner in which the thrower advances in the arena, smartly raising its head-like cameras to survey the shiny white curling sheet, gently cradling and rotating a rock to begin delivery, releasing deftly at the hog line as a skip watches from the backline, with our hopes.Artificial intelligence (AI) today delivers everything from soup recipes to stock predictions, but most tech works out-of-sight. More visible are the physical robots of various shapes, sizes and functions that embody the latest AI technologies. These robots have generally been helpful, and now they are also becoming a more entertaining and enjoyable part of our lives.
This is the post number 20 in the "Deep Reinforcement Learning Explained" series devoted to Reinforcement Learning frameworks. So far, in previous posts, we have been looking at a basic representation of the corpus of RL algorithms (although we have skipped several) that have been relatively easy to program. But from now on, we need to consider both the scale and complexity of the RL algorithms. In this scenario, programming a Reinforcement Learning implementation from scratch can become tedious work with a high risk of programming errors. To address this, the RL community began to build frameworks and libraries to simplify the development of RL algorithms, both by creating new pieces and especially by involving the combination of various algorithmic components.
The "Curly" curling robots are capturing hearts around the world. A product of Korea University in Seoul and the Berlin Institute of Technology, the deep reinforcement learning powered bots slide stones along ice in a winter sport that dates to the 16th century. As much as their human-expert-bettering accuracy or technology impresses, a big part of the Curly appeal is how we see the little machines in the physical space: the determined manner in which the thrower advances in the arena, smartly raising its head-like cameras to survey the shiny white curling sheet, gently cradling and rotating a rock to begin delivery, releasing deftly at the hog line as a skip watches from the backline, with our hopes. Artificial intelligence (AI) today delivers everything from soup recipes to stock predictions, but most tech works out-of-sight. More visible are the physical robots of various shapes, sizes and functions that embody the latest AI technologies. These robots have generally been helpful, and now they are also becoming a more entertaining and enjoyable part of our lives.
The main drawback to reinforcement learning is that it can't be used in some real-life applications. That's because in the process of training themselves, computers initially try just about anything and everything before eventually stumbling on the right path. This initial trial-and-error phase can be problematic for certain applications, such as climate-control systems where abrupt swings in temperature wouldn't be tolerated. The CSEM engineers have developed an approach that overcomes this problem. They showed that computers can first be trained on extremely simplified theoretical models before being set to learn on real-life systems.
Recent advancements in quantum computing have driven the scientific community's quest to solve a certain class of complex problems for which quantum computers would be better suited than traditional supercomputers. To improve the efficiency with which quantum computers can solve these problems, scientists are investigating the use of artificial intelligence approaches. In a new study, scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory have developed a new algorithm based on reinforcement learning to find the optimal parameters for the Quantum Approximate Optimization Algorithm (QAOA), which allows a quantum computer to solve certain combinatorial problems such as those that arise in materials design, chemistry and wireless communications. "Combinatorial optimization problems are those for which the solution space gets exponentially larger as you expand the number of decision variables," said Argonne computer scientist Prasanna Balaprakash. "In one traditional example, you can find the shortest route for a salesman who needs to visit a few cities once by enumerating all possible routes, but given a couple thousand cities, the number of possible routes far exceeds the number of stars in the universe; even the fastest supercomputers cannot find the shortest route in a reasonable time."
It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".