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Found 8 papers, 4 papers with code
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path Planning
In reinforcement learning (RL), exploiting environmental symmetries can significantly enhance efficiency, robustness, and performance.
Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning
Coverage path planning (CPP) is a critical problem in robotics, where the goal is to find an efficient path that covers every point in an area of interest.
Learning to Generate All Feasible Actions
Several machine learning (ML) applications are characterized by searching for an optimal solution to a complex task.
Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation
First, we generate an adapted domain to align the source and target domains on the pixel-level by improving CycleGAN with a multi-scale structured cycle-consistency loss.
Scenic: A Language for Scenario Specification and Data Generation
We design a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time.
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.
Scenic: A Language for Scenario Specification and Scene Generation
We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning.
A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving
The framework supports data collection from both auto-driving scenes and user-configured scenes.
