19 citations · 85 across the 24 of their papers we have counts for
7 papers · 1 filter
Looking Ahead to Avoid Being Late: Solving Hard-Constrained Traveling Salesman Problem
Jingxiao Chen, Ziqin Gong, Minghuan Liu +3
Many real-world problems can be formulated as a constrained Traveling Salesman Problem (TSP). However, the constraints are always complex and numerous, making the TSPs challenging…
MADiff: Offline Multi-agent Learning with Diffusion Models
Zhengbang Zhu, Minghuan Liu, Liyuan Mao +5
Offline reinforcement learning (RL) aims to learn policies from pre-existing datasets without further interactions, making it a challenging task. Q-learning algorithms struggle wit…
Visual Imitation Learning with Patch Rewards
Minghuan Liu, Tairan He, Weinan Zhang +2
Visual imitation learning enables reinforcement learning agents to learn to behave from expert visual demonstrations such as videos or image sequences, without explicit, well-defin…
Planning Immediate Landmarks of Targets for Model-Free Skill Transfer across Agents
Minghuan Liu, Zhengbang Zhu, Menghui Zhu +3
In reinforcement learning applications like robotics, agents usually need to deal with various input/output features when specified with different state/action spaces by their deve…
RITA: Boost Driving Simulators with Realistic Interactive Traffic Flow
Zhengbang Zhu, Shenyu Zhang, Yuzheng Zhuang +11
High-quality traffic flow generation is the core module in building simulators for autonomous driving. However, the majority of available simulators are incapable of replicating tr…
PerfectDou: Dominating DouDizhu with Perfect Information Distillation
Guan Yang, Minghuan Liu, Weijun Hong +4
As a challenging multi-player card game, DouDizhu has recently drawn much attention for analyzing competition and collaboration in imperfect-information games. In this paper, we pr…
Goal-Conditioned Reinforcement Learning: Problems and Solutions
Minghuan Liu, Menghui Zhu, Weinan Zhang
Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems, trains an agent to achieve different goals under particular scenarios. Compared to the stan…