3 papers
cs.LG2026
IMWM: Intuition Models Complement World Models for Latent Planning
Baoqi Gao, Ruize Han, Miao Wang +1
Planning with a learned latent world model is a promising route to control from raw pixels, but a strong world model alone is not enough. We show this experimentally: even with a p…
cs.LG2026
Behavior-Invariant Task Representation Learning with Transformer-based World Models for Offline Meta-Reinforcement Learning
Fuyuan Qian, Menglong Zhang, Song Wang +1
Offline meta-reinforcement learning leverages static datasets to enable agents to generalize to unseen environments by combining offline efficiency with meta-learning adaptability,…
cs.LG2017
Deep Reinforcement Learning with Surrogate Agent-Environment Interface
Song Wang, Yu Jing
In this paper, we propose surrogate agent-environment interface (SAEI) in reinforcement learning. We also state that learning based on probability surrogate agent-environment inter…