97,321 research outputs found
Continual Reinforcement Learning in 3D Non-stationary Environments
High-dimensional always-changing environments constitute a hard challenge for
current reinforcement learning techniques. Artificial agents, nowadays, are
often trained off-line in very static and controlled conditions in simulation
such that training observations can be thought as sampled i.i.d. from the
entire observations space. However, in real world settings, the environment is
often non-stationary and subject to unpredictable, frequent changes. In this
paper we propose and openly release CRLMaze, a new benchmark for learning
continually through reinforcement in a complex 3D non-stationary task based on
ViZDoom and subject to several environmental changes. Then, we introduce an
end-to-end model-free continual reinforcement learning strategy showing
competitive results with respect to four different baselines and not requiring
any access to additional supervised signals, previously encountered
environmental conditions or observations.Comment: Accepted in the CLVision Workshop at CVPR2020: 13 pages, 4 figures, 5
table
Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques
Real-time safety assessment (RTSA) of dynamic systems is a critical task that
has significant implications for various fields such as industrial and
transportation applications, especially in non-stationary environments.
However, the absence of a comprehensive review of real-time safety assessment
methods in non-stationary environments impedes the progress and refinement of
related methods. In this paper, a review of methods and techniques for RTSA
tasks in non-stationary environments is provided. Specifically, the background
and significance of RTSA approaches in non-stationary environments are firstly
highlighted. We then present a problem description that covers the definition,
classification, and main challenges. We review recent developments in related
technologies such as online active learning, online semi-supervised learning,
online transfer learning, and online anomaly detection. Finally, we discuss
future outlooks and potential directions for further research. Our review aims
to provide a comprehensive and up-to-date overview of real-time safety
assessment methods in non-stationary environments, which can serve as a
valuable resource for researchers and practitioners in this field.Comment: Accepted by the 2023 CAA Symposium on Fault Detection, Supervision
and Safety for Technical Processes (SAFEPROCESS 2023
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