From the 1 of 4 linked papers with an AI index.
4 papers
ActSWM: Action-Sensitive World Models for Long-Horizon Planning in Open-World Games
Zhenfeng Gan, ZiTong Zeng, Jiajun Cheng +3
The paper introduces ActSWM, an action-sensitive latent world model that keeps futures distinguishable under different actions, addressing the "context collapse" problem and improv…
OVAL: Open-Vocabulary Augmented Memory Model for Lifelong Object Goal Navigation
Jiahua Pei, Yi Liu, Guoping Pan +3
Object Goal Navigation (ObjectNav) refers to an agent navigating to an object in an unseen environment, which is an ability often required in the accomplishment of complex tasks. W…
Residual Reward Models for Preference-based Reinforcement Learning
Chenyang Cao, Miguel Rogel-GarcÃa, Mohamed Nabail +2
Preference-based Reinforcement Learning (PbRL) provides a way to learn high-performance policies in environments where the reward signal is hard to specify, avoiding heuristic and…
FOSP: Fine-tuning Offline Safe Policy through World Models
Chenyang Cao, Yucheng Xin, Silang Wu +4
Offline Safe Reinforcement Learning (RL) seeks to address safety constraints by learning from static datasets and restricting exploration. However, these approaches heavily rely on…