11 accepted papers
Federated Deep Reinforcement Learning (FDRL) aims to enable distributed collaborative training of multiple DRL models while preserving privacy. Existing FDRL methods function in static client environments, but real-world scenarios often involve dynamic state transitions, such as noise, which render
Offline Federated Deep Reinforcement Learning (FDRL) methods aggregate multiple client-side offline Deep Reinforcement Learning (DRL) models, each trained locally, to facilitate knowledge sharing while preserving privacy. Existing offline FDRL methods assign client weights during global aggregation …
Deep reinforcement learning (DRL) has been widely applied to various applications, but improving exploration remains a key challenge. Recently, multi-actor DRL has emerged as a promising approach that enhances exploration by simultaneously deploying multiple actors for learning. Among these methods,…
Causal inconsistency arises when the underlying causal graphs captured by generative models like Normalizing Flows are inconsistent with those specified in causal models like Struct Causal Models. This inconsistency can cause unwanted issues including unfairness. Prior works to achieve causal consis…
To avoid dangerous situations, such as collisions in dynamic environments, autonomous vehicles must predict the risks of the current scene to take safe actions. Traditional rule-based risk prediction methods and existing reinforcement learning (RL) approaches, which typically rely on manually design
Fair principal component analysis (FPCA), a ubiquitous dimensionality reduction technique in signal processing and machine learning, aims to find a low-dimensional representation for a high-dimensional dataset in view of fairness. The FPCA problem involves optimizing a non-convex and non-smooth func…
Recent advancements have introduced machine learning frameworks to enhance the Branch and Bound (B\&B) branching policies for solving Mixed Integer Linear Programming (MILP). These methods, primarily relying on imitation learning of Strong Branching, have shown superior performance. However, collect…
This letter introduces <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Nav</b>igation <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tr</b>ansformer (NavTr), a novel framework for object-goal navigation
Distribution-to-distribution-based lidar odometry is known for its good accuracy, while it cannot run in real-time when the number of points is large. To alleviate this problem, Faster Generalized Iterative Closest Point (FasterGICP) is proposed in this letter, in which an acceptance-rejection sampl
Loop closure can effectively eliminate the accumulated error and plays an important role in Simultaneous Localization and Mapping (SLAM). There remains challenges in loop detection and loop correction due to the large viewpoints difference and the environment appearance changes. In this letter, we p
Visual relocalization plays an important role in computer vision and robotics. However, feature ambiguities have made it remain challenging. In this work, we propose a novel regression forest based visual relocalization method that is performed in a coarse-to-fine manner. A topological regression tr