Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of Beamlines

Synchrotron radiation sources play a crucial role in fields such as materials science, biology, and chemistry. The beamline, a key subsystem of the synchrotron, modulates and directs the radiation to the sample for analysis. However, the alignment of beamlines is a complex and time-consuming process, primarily carried out manually by experienced engineers. Even minor misalignments in optical components can significantly affect the beam's properties, leading to suboptimal experimental outcomes. Current automated methods, such as bayesian optimization (BO) and reinforcement learning (RL), although these methods enhance performance, limitations remain. The relationship between the current and target beam properties, crucial for determining the adjustment, is not fully considered. Additionally, the physical characteristics of optical elements are overlooked, such as the need to adjust specific devices to control the output beam's spot size or position. This paper addresses the alignment of beamlines by modeling it as a Markov Decision Process (MDP) and training an intelligent agent using RL. The agent calculates adjustment values based on the current and target beam states, executes actions, and iterates until optimal parameters are achieved. A policy network with action attention is designed to improve decision-making by considering both state differences and the impact of optical components. Experiments on two simulated beamlines demonstrate that our algorithm outperforms existing methods, with ablation studies highlighting the effectiveness of the action attention-based policy network.

Semantic2Graph: Graph-based Multi-modal Feature for Action Segmentation in Videos

Video action segmentation and recognition tasks have been widely applied in many fields. Most previous studies employ large-scale, high computational visual models to understand videos comprehensively. However, few studies directly employ the graph model to reason about the video. The graph model provides the benefits of fewer parameters, low computational cost, a large receptive field, and flexible neighborhood message aggregation. In this paper, we present a graph-based method named Semantic2Graph, to turn the video action segmentation and recognition problem into node classification of graphs. To preserve fine-grained relations in videos, we construct the graph structure of videos at the frame-level and design three types of edges: temporal, semantic, and self-loop. We combine visual, structural, and semantic features as node attributes. Semantic edges are used to model long-term spatio-temporal relations, while the semantic features are the embedding of the label-text based on the textual prompt. A Graph Neural Networks (GNNs) model is used to learn multi-modal feature fusion. Experimental results show that Semantic2Graph achieves improvement on GTEA and 50Salads, compared to the state-of-the-art results. Multiple ablation experiments further confirm the effectiveness of semantic features in improving model performance, and semantic edges enable Semantic2Graph to capture long-term dependencies at a low cost.