313 citations · 436 across the 14 of their papers we have counts for
14 papers
Rewarding Beliefs, Not Actions: Consistency-Guided Credit Assignment for Long-Horizon Agents
Wenjie Tang, Minne Li, Sijie Huang +2
Reinforcement learning from verifiable rewards (RLVR) is a promising paradigm for improving large language model (LLM) agents on long-horizon interactive tasks. However, in partial…
DSGBench: A Diverse Strategic Game Benchmark for Evaluating LLM-based Agents in Complex Decision-Making Environments
Wenjie Tang, Yuan Zhou, Erqiang Xu +3
Large language model (LLM)-based agents are increasingly applied to complex strategic environments that demand long-horizon reasoning, multi-agent interaction, and decision-making…
PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement Learning
Kun Hu, Muning Wen, Xihuai Wang +5
Multi-agent reinforcement learning (MARL) faces challenges in coordinating agents due to complex interdependencies within multi-agent systems. Most MARL algorithms use the simultan…
A Game-Theoretic Approach to Multi-Agent Trust Region Optimization
Ying Wen, Hui Chen, Yaodong Yang +4
Trust region methods are widely applied in single-agent reinforcement learning problems due to their monotonic performance-improvement guarantee at every iteration. Nonetheless, wh…
Learning in Nonzero-Sum Stochastic Games with Potentials
David Mguni, Yutong Wu, Yali Du +6
Multi-agent reinforcement learning (MARL) has become effective in tackling discrete cooperative game scenarios. However, MARL has yet to penetrate settings beyond those modelled by…
Causal World Models by Unsupervised Deconfounding of Physical Dynamics
Minne Li, Mengyue Yang, Furui Liu +3
The capability of imagining internally with a mental model of the world is vitally important for human cognition. If a machine intelligent agent can learn a world model to create a…
Compositional ADAM: An Adaptive Compositional Solver
Rasul Tutunov, Minne Li, Alexander I. Cowen-Rivers +2
In this paper, we present C-ADAM, the first adaptive solver for compositional problems involving a non-linear functional nesting of expected values. We proof that C-ADAM converges…
Multi-View Reinforcement Learning
Minne Li, Lisheng Wu, Haitham Bou Ammar +1
This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models…
Bi-level Actor-Critic for Multi-agent Coordination
Haifeng Zhang, Weizhe Chen, Zeren Huang +4
Coordination is one of the essential problems in multi-agent systems. Typically multi-agent reinforcement learning (MARL) methods treat agents equally and the goal is to solve the…
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms
Jiarui Jin, Ming Zhou, Weinan Zhang +9
How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-h…
Joint Perception and Control as Inference with an Object-based Implementation
Minne Li, Zheng Tian, Pranav Nashikkar +3
Existing model-based reinforcement learning methods often study perception modeling and decision making separately. We introduce joint Perception and Control as Inference (PCI), a…
Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning
Minne Li, Zhiwei, Qin +7
A fundamental question in any peer-to-peer ridesharing system is how to, both effectively and efficiently, dispatch user's ride requests to the right driver in real time. Tradition…
Learning Shared Dynamics with Meta-World Models
Lisheng Wu, Minne Li, Jun Wang
Humans have consciousness as the ability to perceive events and objects: a mental model of the world developed from the most impoverished of visual stimuli, enabling humans to make…
Mean Field Multi-Agent Reinforcement Learning
Yaodong Yang, Rui Luo, Minne Li +3
Existing multi-agent reinforcement learning methods are limited typically to a small number of agents. When the agent number increases largely, the learning becomes intractable due…