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In recent years, the aging population in Chinese cities has become increasingly severe. In 2021, the proportion of people aged 65 and above in Wuchang District, Wuhan reached 24.38%, indicating a deeply aging society. Consequently, the importance of studying aging-friendly public spaces in communities is becoming more prominent. This research was conducted based on survey data of outdoor spaces in Shouyi Community, Shouyi Road. Factor analysis and IPA were used to assess the satisfaction and importance of elderly residents' satisfaction. The primary renovation factors identified include route organization, site height difference protection, barrier-free design, plant color matching, plant configuration, and landscape features, followed by spatial continuity, lighting, microclimate, and rest facilities layout. These analyses effectively identified public factors and prioritize the aging-friendly renovation of public spaces in old communities. In the renovation of old communities in Wuhan, safety is the primary renovation factor for aging-friendly communities. Improving site height difference protection and barrier-free facilities significantly enhances residents' satisfaction. The aesthetic improvement of outdoor spaces is the second renovation factor, indicating that the elderly increasingly value the aesthetic aspect of spaces. Although accessibility of community public spaces is the third renovation factor, route organization at the criterion level has the highest priority. Road traffic and parking difficulties are also key factors in the renovation of old communities. [ABSTRACT FROM AUTHOR]

Policy evaluation (PE) is a critical sub‐problem in reinforcement learning, which estimates the value function for a given policy and can be used for policy improvement. However, there still exist some limitations in current PE methods, such as low sample efficiency and local convergence, especially on complex tasks. In this study, a novel PE algorithm called Least‐Squares Truncated Temporal‐Difference learning (LST2D) is proposed. In LST2D, an adaptive truncation mechanism is designed, which effectively takes advantage of the fast convergence property of Least‐Squares Temporal Difference learning and the asymptotic convergence property of Temporal Difference learning (TD). Then, two feature pre‐training methods are utilised to improve the approximation ability of LST2D. Furthermore, an Actor‐Critic algorithm based on LST2D and pre‐trained feature representations (ACLPF) is proposed, where LST2D is integrated into the critic network to improve learning‐prediction efficiency. Comprehensive simulation studies were conducted on four robotic tasks, and the corresponding results illustrate the effectiveness of LST2D. The proposed ACLPF algorithm outperformed DQN, ACER and PPO in terms of sample efficiency and stability, which demonstrated that LST2D can be applied to online learning control problems by incorporating it into the actor‐critic architecture. [ABSTRACT FROM AUTHOR]

Here, the challenges of sample efficiency and navigation performance in deep reinforcement learning for visual navigation are focused and a deep imitation reinforcement learning approach is proposed. Our contributions are mainly three folds: first, a framework combining imitation learning with deep reinforcement learning is presented, which enables a robot to learn a stable navigation policy faster in the target‐driven navigation task. Second, the surrounding images is taken as the observation instead of sequential images, which can improve the navigation performance for more information. Moreover, a simple yet efficient template matching method is adopted to determine the stop action, making the system more practical. Simulation experiments in the AI‐THOR environment show that the proposed approach outperforms previous end‐to‐end deep reinforcement learning approaches, which demonstrate the effectiveness and efficiency of our approach. [ABSTRACT FROM AUTHOR]

Traffic sign recognition plays an important role in autonomous vehicles as well as advanced driver assistance systems. Although various methods have been developed, it is still difficult for the state-of-the-art algorithms to obtain high recognition precision with low computational costs. In this paper, based on the investigation on the influence that color spaces have on the representation learning of convolutional neural network, a novel traffic sign recognition approach called DP-KELM is proposed by using a kernel-based extreme learning machine (KELM) classifier with deep perceptual features. Unlike the previous approaches, the representation learning process in DP-KELM is implemented in the perceptual Lab color space. Based on the learned deep perceptual feature, a kernel-based ELM classifier is trained with high computational efficiency and generalization performance. Through the experiments on the German traffic sign recognition benchmark, the proposed method is demonstrated to have higher precision than most of the state-of-the-art approaches. In particular, when compared with the hinge loss stochastic gradient descent method which has the highest precision, the proposed method can achieve a comparable recognition rate with significantly fewer computational costs. [ABSTRACT FROM PUBLISHER]