170 citations · 330 across the 27 of their papers we have counts for
5 papers · 1 filter
Mixture-of-World Models: Scaling Multi-Task Reinforcement Learning with Modular Latent Dynamics
Boxuan Zhang, Weipu Zhang, Zhaohan Feng +4
A fundamental challenge in multi-task reinforcement learning (MTRL) is achieving sample efficiency in visual domains where tasks exhibit substantial heterogeneity in both observati…
DyMoDreamer: World Modeling with Dynamic Modulation
Boxuan Zhang, Runqing Wang, Wei Xiao +5
A critical bottleneck in deep reinforcement learning (DRL) is sample inefficiency, as training high-performance agents often demands extensive environmental interactions. Model-bas…
STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning
Weipu Zhang, Gang Wang, Jian Sun +2
Recently, model-based reinforcement learning algorithms have demonstrated remarkable efficacy in visual input environments. These approaches begin by constructing a parameterized s…
Soft Decomposed Policy-Critic: Bridging the Gap for Effective Continuous Control with Discrete RL
Yechen Zhang, Jian Sun, Gang Wang +2
Discrete reinforcement learning (RL) algorithms have demonstrated exceptional performance in solving sequential decision tasks with discrete action spaces, such as Atari games. How…
Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning
Runqing Wang, Gang Wang, Jian Sun +2
Flexible manufacturing has given rise to complex scheduling problems such as the flexible job shop scheduling problem (FJSP). In FJSP, operations can be processed on multiple machi…