Search Results for author:
Found 4 papers, 2 papers with code
Scale-Invariant Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning
This novel transformation process ensures that the converted constraints are invariant to the objective scales while having the same effect as the original objectives.
Multi-Objective Reinforcement Learning
reinforcement-learning
Renderable Neural Radiance Map for Visual Navigation
We propose a novel type of map for visual navigation, a renderable neural radiance map (RNR-Map), which is designed to contain the overall visual information of a 3D environment.
Visual Graph Memory With Unsupervised Representation for Visual Navigation
We present a novel graph-structured memory for visual navigation, called visual graph memory (VGM), which consists of unsupervised image representations obtained from navigation history.
Robustness of Object Recognition under Extreme Occlusion in Humans and Computational Models
We test both humans and the above-mentioned computational models in a challenging task of object recognition under extreme occlusion, where target objects are heavily occluded by irrelevant real objects in real backgrounds.
