4 papers
Reparameterization Flow Policy Optimization
Hai Zhong, Zhuoran Li, Xun Wang +1
Reparameterization Policy Gradient (RPG) has emerged as a powerful paradigm for model-based reinforcement learning, enabling high sample efficiency by backpropagating gradients thr…
From Solo to Symphony: Orchestrating Multi-Agent Collaboration with Single-Agent Demos
Xun Wang, Zhuoran Li, Yanshan Lin +2
Training a team of agents from scratch in multi-agent reinforcement learning (MARL) is highly inefficient, much like asking beginners to play a symphony together without first prac…
Reparameterization Proximal Policy Optimization
Hai Zhong, Xun Wang, Zhuoran Li +1
By leveraging differentiable dynamics, Reparameterization Policy Gradient (RPG) achieves high sample efficiency. However, current approaches are hindered by two critical limitation…
Beyond Shallow Behavior: Task-Efficient Value-Based Multi-Task Offline MARL via Skill Discovery
Xun Wang, Zhuoran Li, Hai Zhong +1
As a data-driven approach, offline MARL learns superior policies solely from offline datasets, ideal for domains rich in historical data but with high interaction costs and risks.…