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Register a new userAnomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning
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We consider the problem of detecting anomalies among a given set of processes using their noisy binary sensor measurements. The noiseless sensor measurement corresponding to a normal process is 0, and the measurement is 1 if the process is anomalous. The decision-making algorithm is assumed to have no knowledge of the number of anomalous processes. The algorithm is allowed to choose a subset of the sensors at each time instant until the confidence level on the decision exceeds the desired value. Our objective is to design a sequential sensor selection policy that dynamically determines which processes to observe at each time and when to terminate the detection algorithm. The selection policy is designed such that the anomalous processes are detected with the desired confidence level while incurring minimum cost which comprises the delay in detection and the cost of sensing. We cast this problem as a sequential hypothesis testing problem within the framework of Markov decision processes, and solve it using the actor-critic deep reinforcement learning algorithm. This deep neural network-based algorithm offers a low complexity solution with good detection accuracy. We also study the effect of statistical dependence between the processes on the algorithm performance. Through numerical experiments, we show that our algorithm is able to adapt to any unknown statistical dependence pattern of the processes.
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Reinforcement Learning (RL) and its integration with deep learning have achieved impressive performance in various robotic control tasks, ranging from motion planning and navigation to end-to-end visual manipulation. However, stability is not guaranteed in model-free RL by solely using data. From a control-theoretic perspective, stability is the most important property for any control system, since it is closely related to safety, robustness, and reliability of robotic systems. In this paper, we propose an actor-critic RL framework for control which can guarantee closed-loop stability by employing the classic Lyapunovs method in control theory. First of all, a data-based stability theorem is proposed for stochastic nonlinear systems modeled by Markov decision process. Then we show that the stability condition could be exploited as the critic in the actor-critic RL to learn a controller/policy. At last, the effectiveness of our approach is evaluated on several well-known 3-dimensional robot control tasks and a synthetic biology gene network tracking task in three different popular physics simulation platforms. As an empirical evaluation on the advantage of stability, we show that the learned policies can enable the systems to recover to the equilibrium or way-points when interfered by uncertainties such as system parametric variations and external disturbances to a certain extent.
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In this paper, we introduce a new scheduling algorithm MARS based on a cost-aware multi-scalable reinforcement learning approach, which serves as an intermediate layer between HPC resource manager and user application workflow, MARS ensembles the pre-generated models from users workflows and decides on the most suitable strategy for optimization. A whole workflow application would be split into several optimized subtasks. Then based on a pre-defined resource management plan. A reward will be generated after executing a scheduled task. Lastly, MARS updates the Deep Neural Network (DNN) model for future use. MARS is designed to be able to optimize the existing models through the reinforcement mechanism. MARS can adapt to the shortage of training samples and optimize the performance by itself, especially through combining the small tasks together or switching between pre-built scheduling strategy such as Backfilling, SJF, etc, then choosing the most suitable approach. We tested MARS using different real-world workflow traces. MARS can achieve between 5%-60% better performance while comparing to the other approaches.
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