Reinforcement learning (RL) often requires decomposing a problem into subtasks and composing learned behaviors on these tasks. Compositionality in RL has the potential to create modular subtask units that interface with other system capabilities. However, generating compositional models requires the characterization of minimal assumptions for the robustness of the compositional feature. We develop a framework for a \emph{compositional theory} of RL using a categorical point of view. Given the categorical representation of compositionality, we investigate sufficient conditions under which learning-by-parts results in the same optimal policy as learning on the whole. In particular, our approach introduces a category $\mathsf{MDP}$, whose objects are Markov decision processes (MDPs) acting as models of tasks. We show that $\mathsf{MDP}$ admits natural compositional operations, such as certain fiber products and pushouts. These operations make explicit compositional phenomena in RL and unify existing constructions, such as puncturing hazardous states in composite MDPs and incorporating state-action symmetry. We also model sequential task completion by introducing the language of zig-zag diagrams that is an immediate application of the pushout operation in $\mathsf{MDP}$.

I recently attended the 15th annual conference on Artificial Generally Intelligence (AGI-22) that was held in Seattle this August, in an attempt to familiarize myself with new developments that could lead to the eventual creation of an Artificial General Intelligence (AGI). An AGI is a type of advanced AI that can generalize across multiple domains and is not narrow in scope. Examples of narrow AI include an autonomous vehicle, a chatbot, a chess bot, or any other AI which is designed for a single purpose. An AGI in comparison would be able to flexibly alternate between any of the above or any other field of expertise. It consists of a speculative type of AI that would take advantage of nascent algorithms such as transfer learning, and evolutionary learning, while also exploiting legacy algorithms such as deep reinforcement learning.

Autonomous vehicles have the potential to lower the accident rate when compared to human driving. Moreover, it is the driving force of the automated vehicles' rapid development over the last few years. In the higher Society of Automotive Engineers (SAE) automation level, the vehicle's and passengers' safety responsibility is transferred from the driver to the automated system, so thoroughly validating such a system is essential. Recently, academia and industry have embraced scenario-based evaluation as the complementary approach to road testing, reducing the overall testing effort required. It is essential to determine the system's flaws before deploying it on public roads as there is no safety driver to guarantee the reliability of such a system. This paper proposes a Reinforcement Learning (RL) based scenario-based falsification method to search for a high-risk scenario in a pedestrian crossing traffic situation. We define a scenario as risky when a system under testing (SUT) does not satisfy the requirement. The reward function for our RL approach is based on Intel's Responsibility Sensitive Safety(RSS), Euclidean distance, and distance to a potential collision.

This scientific paper propose a novel portfolio optimization model using an improved deep reinforcement learning algorithm. The objective function of the optimization model is the weighted sum of the expectation and value at risk(VaR) of portfolio cumulative return. The proposed algorithm is based on actor-critic architecture, in which the main task of critical network is to learn the distribution of portfolio cumulative return using quantile regression, and actor network outputs the optimal portfolio weight by maximizing the objective function mentioned above. Meanwhile, we exploit a linear transformation function to realize asset short selling. Finally, A multi-process method is used, called Ape-x, to accelerate the speed of deep reinforcement learning training. To validate our proposed approach, we conduct backtesting for two representative portfolios and observe that the proposed model in this work is superior to the benchmark strategies.

Although Deep Neural Networks (DNN) have become the backbone technology of several ubiquitous applications, their deployment in resource-constrained machines, e.g., Internet of Things (IoT) devices, is still challenging. To satisfy the resource requirements of such a paradigm, collaborative deep inference with IoT synergy was introduced. However, the distribution of DNN networks suffers from severe data leakage. Various threats have been presented, including black-box attacks, where malicious participants can recover arbitrary inputs fed into their devices. Although many countermeasures were designed to achieve privacy-preserving DNN, most of them result in additional computation and lower accuracy. In this paper, we present an approach that targets the security of collaborative deep inference via re-thinking the distribution strategy, without sacrificing the model performance. Particularly, we examine different DNN partitions that make the model susceptible to black-box threats and we derive the amount of data that should be allocated per device to hide proprieties of the original input. We formulate this methodology, as an optimization, where we establish a trade-off between the latency of co-inference and the privacy-level of data. Next, to relax the optimal solution, we shape our approach as a Reinforcement Learning (RL) design that supports heterogeneous devices as well as multiple DNNs/datasets.

Object Goal Navigation requires a robot to find and navigate to an instance of a target object class in a previously unseen environment. Our framework incrementally builds a semantic map of the environment over time, and then repeatedly selects a long-term goal ('where to go') based on the semantic map to locate the target object instance. Long-term goal selection is formulated as a vision-based deep reinforcement learning problem. Specifically, an Encoder Network is trained to extract high-level features from a semantic map and select a long-term goal. In addition, we incorporate data augmentation and Q-function regularization to make the long-term goal selection more effective. We report experimental results using the photo-realistic Gibson benchmark dataset in the AI Habitat 3D simulation environment to demonstrate substantial performance improvement on standard measures in comparison with a state of the art data-driven baseline.

A robot dog can learn to walk on unfamiliar and hard-to-master terrain, such as grass, bark and hiking trails, in just 20 minutes, thanks to a machine learning algorithm. Most autonomous robots have to be carefully programmed by humans or extensively tested in simulated scenarios before they can perform real-world tasks, such as walking up a rocky hill or a slippery slope – and when they encounter unfamiliar environments, they tend to struggle. Now, Sergey Levine at the University of California, Berkeley, and his colleagues have demonstrated that a robot using a kind of machine learning called deep reinforcement learning can work out how to walk in about 20 minutes in several different environments, such as a grass lawn, a layer of bark, a memory foam mattress and a hiking trail. The robot uses an algorithm called Q-learning, which doesn't require a working model of the target terrain. Such machine learning algorithms are usually used in simulations.

The widespread adoption of distributed energy resources, and the advent of smart grid technologies, have allowed traditionally passive power system users to become actively involved in energy trading. Recognizing the fact that the traditional centralized grid-driven energy markets offer minimal profitability to these users, recent research has shifted focus towards decentralized peer-to-peer (P2P) energy markets. In these markets, users trade energy with each other, with higher benefits than buying or selling to the grid. However, most researches in P2P energy trading largely overlook the user perception in the trading process, assuming constant availability, participation, and full compliance. As a result, these approaches may result in negative attitudes and reduced engagement over time. In this paper, we design an automated P2P energy market that takes user perception into account. We employ prospect theory to model the user perception and formulate an optimization framework to maximize the buyer's perception while matching demand and production. Given the non-linear and non-convex nature of the optimization problem, we propose Differential Evolution-based Algorithm for Trading Energy called DEbATE. Additionally, we introduce a risk-sensitive Q-learning algorithm, named Pricing mechanism with Q-learning and Risk-sensitivity (PQR), which learns the optimal price for sellers considering their perceived utility. Results based on real traces of energy consumption and production, as well as realistic prospect theory functions, show that our approach achieves a 26% higher perceived value for buyers and generates 7% more reward for sellers, compared to a recent state of the art approach.

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open source datasets and tools, major findings, challenges, and future directions. Discussions focus on current methods of uncertainty quantification and optimization and how they are applied in different dimensions of a digital twin. Additionally, this paper presents a case study where a battery digital twin is constructed and tested to illustrate some of the modeling and twinning methods reviewed in this two-part review. Code and preprocessed data for generating all the results and figures presented in the case study are available on GitHub.

Insertion of hardware Trojans (HTs) in integrated circuits is a pernicious threat. Since HTs are activated under rare trigger conditions, detecting them using random logic simulations is infeasible. In this work, we design a reinforcement learning (RL) agent that circumvents the exponential search space and returns a minimal set of patterns that is most likely to detect HTs. Experimental results on a variety of benchmarks demonstrate the efficacy and scalability of our RL agent, which obtains a significant reduction ($169\times$) in the number of test patterns required while maintaining or improving coverage ($95.75\%$) compared to the state-of-the-art techniques.