3 papers
cs.AI2026
RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations
Yuanpeng He, Ceyao Zhang, Fangjing Li +8
Recent world action models draw on video generation priors to jointly predict future observations and robot actions. Adapting these priors to control requires determining how futur…
cs.AI2025
Multi-Agent Guided Policy Optimization
Yueheng Li, Guangming Xie, Zongqing Lu
Due to practical constraints such as partial observability and limited communication, Centralized Training with Decentralized Execution (CTDE) has become the dominant paradigm in c…
cs.LG2025
Guided Policy Optimization under Partial Observability
Yueheng Li, Guangming Xie, Zongqing Lu
Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty. While additional information, suc…