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Found 17 papers, 3 papers with code
Scrutinize What We Ignore: Reining Task Representation Shift In Context-Based Offline Meta Reinforcement Learning
Previous context-based approaches predominantly rely on the intuition that maximizing the mutual information between the task and the task representation ($I(Z;M)$) can lead to performance improvements.
POWQMIX: Weighted Value Factorization with Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learning
Value function factorization methods are commonly used in cooperative multi-agent reinforcement learning, with QMIX receiving significant attention.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning
As a marriage between offline RL and meta-RL, the advent of offline meta-reinforcement learning (OMRL) has shown great promise in enabling RL agents to multi-task and quickly adapt while acquiring knowledge safely.
VNI-Net: Vector Neurons-based Rotation-Invariant Descriptor for LiDAR Place Recognition
To address the significant information loss when formulating rotation-invariant descriptors, we propose computing distances between features at different layers within the Euclidean space neighborhood.
Computational Efficiency
Simultaneous Localization and Mapping
Safe Reinforcement Learning with Dead-Ends Avoidance and Recovery
In this paper, we propose a method to construct a boundary that discriminates safe and unsafe states.
Learning Sequence Descriptor based on Spatio-Temporal Attention for Visual Place Recognition
Visual Place Recognition (VPR) aims to retrieve frames from a geotagged database that are located at the same place as the query frame.
DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization
The states of the static and dynamic objects and ego vehicle in the sliding window are integrated into a unified local optimization framework.
Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy for place recognition
Visual Place Recognition (VPR) in areas with similar scenes such as urban or indoor scenarios is a major challenge.
DSC: Deep Scan Context Descriptor for Large-Scale Place Recognition
LiDAR-based place recognition is an essential and challenging task both in loop closure detection and global relocalization.
Unsupervised Joint Learning of Depth, Optical Flow, Ego-motion from Video
Compared with independent training, joint training can make full use of the geometric relationship between geometric elements and provide dynamic and static information of the scene.
Dense Dual-Path Network for Real-time Semantic Segmentation
We design a light-weight and powerful backbone with dense connectivity to facilitate feature reuse throughout the whole network and the proposed Dual-Path module (DPM) to sufficiently aggregate multi-scale contexts.
Detecting Lane and Road Markings at A Distance with Perspective Transformer Layers
This is due to the fact that distant lane and road markings occupy a small number of pixels in the image, and scales of lane and road markings are inconsistent at various distances and perspectives.
Occlusion Aware Unsupervised Learning of Optical Flow From Video
In this paper, we proposed an unsupervised learning method for estimating the optical flow between video frames, especially to solve the occlusion problem.
Real-time Multi-target Path Prediction and Planning for Autonomous Driving aided by FCN
Real-time multi-target path planning is a key issue in the field of autonomous driving.
Vision-based Semantic Mapping and Localization for Autonomous Indoor Parking
In this paper, we proposed a novel and practical solution for the real-time indoor localization of autonomous driving in parking lots.
VH-HFCN based Parking Slot and Lane Markings Segmentation on Panoramic Surround View
At the same time, we proposed a highly fused convolutional network (HFCN) based segmentation method for parking slot and lane markings based on the PSV dataset.
Semantic Segmentation via Highly Fused Convolutional Network with Multiple Soft Cost Functions
We evaluate our model on three major segmentation datasets: CamVid, PASCAL VOC and ADE20K.
