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.

State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China Quantum computing promises to enhance machine learning and artificial intelligence [1-3]. Different quantum algorithms have been proposed to improve a wide spectrum of machine learning tasks [4-12]. Yet, recent theoretical works show that, similar to traditional classifiers based on deep classical neural networks, quantum classifiers would suffer from the vulnerability problem: adding tiny carefully-crafted perturbations to the legitimate original data samples would facilitate incorrect predictions at a notably high confidence level [13-17]. This will pose serious problems for future quantum machine learning applications in safety and security-critical scenarios [18-20]. Here, we report the first experimental demonstration of quantum adversarial learning with programmable superconducting qubits. We train quantum classifiers, which are built upon variational quantum circuits consisting of ten transmon qubits featuring average lifetimes of 150 µs, and average fidelities of simultaneous single-and two-qubit gates above 99.94% and 99.4% respectively, with both real-life images (e.g., medical magnetic resonance imaging scans) and quantum data. We demonstrate that these well-trained classifiers (with testing accuracy up to 99%) can be practically deceived by small adversarial perturbations, whereas an adversarial training process would significantly enhance their robustness to such perturbations. Our results reveal experimentally a crucial vulnerability aspect of quantum learning systems under adversarial scenarios and demonstrate an effective defense strategy against adversarial attacks, which provide a valuable guide for quantum artificial intelligence applications with both near-term and future quantum devices. In recent years, artificial intelligence (AI) [21-23] and been proposed to enhance the robustness of quantum classifiers quantum computing [24-26] have made dramatic progress. However, demonstrating Their intersection gives rise to a research frontier called, quantum adversarial examples for quantum classifiers experimentally machine learning or generally, quantum AI [1-3]. A number and showing the effectiveness of the proposed countermeasures of quantum algorithms have been proposed to enhance in practice are challenging and have not previously various AI tasks [4-12].

My objective with this article is to demystify a few foundational Reinforcement Learning (RL) concepts with hands-on examples. We are going to apply RL to the infamous Glass Bridge challenge from the Netflix series Squid Game episode 7. Although no previous RL knowledge is required, solid Python coding skills and basic machine learning understanding are necessary to follow the content of this article. The code can be found here. In simple words, RL is a computational approach used to achieve a pre-defined goal, which can be winning a chess game, optimizing a medical treatment, or improving a financial trading strategy.

DeepMind, the UK-based subsidiary of Alphabet, Google's parent company, has taught artificial intelligence how to control a nuclear fusion reactor. The company announced on Feb. 16 that it had used AI to successfully control superheated matter inside a nuclear fusion reactor, and their findings are detailed in a paper published in the journal, Nature. DeepMind whose long-term goal is to "solve intelligence, developing more general and capable problem-solving systems, known as artificial general intelligence (AGI)" was launched in 2010 and acquired by Google in 2014. The scientific discovery company collaborated with the nuclear fusion research lab, the Swiss Plasma Center at École Polytechnique Fédérale de Lausanne on the project. Together, they have "developed a new magnetic control method for plasmas based on deep reinforcement learning" which they applied to a real-world plasma for the first time in the SPC's tokamak research facility, called TCV.

Unsupervised Reinforcement Learning (RL), where RL agents pre-train with self-supervised rewards, is an emerging paradigm for developing RL agents that are capable of generalization. Recently, we released the Unsupervised RL Benchmark (URLB) which we covered in a previous post. A surprising finding was that competence-based algorithms significantly underperformed other categories. In this post we will demystify what has been holding back competence-based methods and introduce Contrastive Intrinsic Control (CIC), a new competence-based algorithm that is the first to achieve leading results on URLB. To recap, competence-based methods (which we will cover in detail) maximize the mutual information between states and skills (e.g.

Satisfying safety constraints almost surely (or with probability one) can be critical for deployment of Reinforcement Learning (RL) in real-life applications. For example, plane landing and take-off should ideally occur with probability one. We address the problem by introducing Safety Augmented (Saute) Markov Decision Processes (MDPs), where the safety constraints are eliminated by augmenting them into the state-space and reshaping the objective. We show that Saute MDP satisfies the Bellman equation and moves us closer to solving Safe RL with constraints satisfied almost surely. We argue that Saute MDP allows to view Safe RL problem from a different perspective enabling new features.

Reinforcement Learning (RL), a field of machine learning, is based on the principle of trial and error. In easier words, it learns from its own mistakes and corrects the mistake. The aim is simply to build a strategy to guide the intelligent agent to take action in a sequence that leads to fulfilling some ultimate goal. Autonomous Driving (AD) uses Deep Reinforcement Learning (DRL) to make real-time decisions and strategies, not only in AD but also in the field of sales, management and many others. In this article, we will mainly discuss how RL can be used in transportation for better intelligent solutions.

Manipulation of deformable objects is a challenging task for a robot. It will be problematic to use a single sensory input to track the behaviour of such objects: vision can be subjected to occlusions, whereas tactile inputs cannot capture the global information that is useful for the task. In this paper, we study the problem of using vision and tactile inputs together to complete the task of following deformable linear objects, for the first time. We create a Reinforcement Learning agent using different sensing modalities and investigate how its behaviour can be boosted using visual-tactile fusion, compared to using a single sensing modality. To this end, we developed a benchmark in simulation for manipulating the deformable linear objects using multimodal sensing inputs. The policy of the agent uses distilled information, e.g., the pose of the object in both visual and tactile perspectives, instead of the raw sensing signals, so that it can be directly transferred to real environments. In this way, we disentangle the perception system and the learned control policy. Our extensive experiments show that the use of both vision and tactile inputs, together with proprioception, allows the agent to complete the task in up to 92% of cases, compared to 77% when only one of the signals is given. Our results can provide valuable insights for the future design of tactile sensors and for deformable objects manipulation.

The improvements in the edge computing technology pave the road for diversified applications that demand real-time interaction. However, due to the mobility of the end-users and the dynamic edge environment, it becomes challenging to handle the task offloading with high performance. Moreover, since each application in mobile devices has different characteristics, a task orchestrator must be adaptive and have the ability to learn the dynamics of the environment. For this purpose, we develop a deep reinforcement learning based task orchestrator, DeepEdge, which learns to meet different task requirements without needing human interaction even under the heavily-loaded stochastic network conditions in terms of mobile users and applications. Given the dynamic offloading requests and time-varying communication conditions, we successfully model the problem as a Markov process and then apply the Double Deep Q-Network (DDQN) algorithm to implement DeepEdge. To evaluate the robustness of DeepEdge, we experiment with four different applications including image rendering, infotainment, pervasive health, and augmented reality in the network under various loads. Furthermore, we compare the performance of our agent with the four different task offloading approaches in the literature. Our results show that DeepEdge outperforms its competitors in terms of the percentage of satisfactorily completed tasks.

Reinforcement learning (RL) has shown promise in creating complex logic in controlled settings. On the other hand, what are the prospects for using RL in a more complicated context like telecom networks? Let's learn the basics first. What is reinforcement learning, and how does it work? In machine learning, the three methodologies are reinforcement learning (RL), supervised learning, and unsupervised learning.