149 citations · 219 across the 20 of their papers we have counts for
22 papers
Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixes
Yuqian Fu, Haohuan Huang, Kaiwen Jiang +4
On-policy distillation (OPD) is increasingly used in LLM post-training because it can leverage a teacher model to provide dense supervision on student rollouts. The standard implem…
Equilibrium Policy Generalization: A Reinforcement Learning Framework for Cross-Graph Zero-Shot Generalization in Pursuit-Evasion Games
Runyu Lu, Peng Zhang, Ruochuan Shi +5
Equilibrium learning in adversarial games is an important topic widely examined in the fields of game theory and reinforcement learning (RL). Pursuit-evasion game (PEG), as an impo…
CriticSearch: Fine-Grained Credit Assignment for Search Agents via a Retrospective Critic
Yaocheng Zhang, Haohuan Huang, Zijun Song +4
Tool-Integrated Reasoning (TIR) with search engines enables large language models to iteratively retrieve up-to-date external knowledge, enhancing adaptability and generalization i…
ARAC: Adaptive Regularized Multi-Agent Soft Actor-Critic in Graph-Structured Adversarial Games
Ruochuan Shi, Runyu Lu, Yuanheng Zhu +1
In graph-structured multi-agent reinforcement learning (MARL) adversarial tasks such as pursuit and confrontation, agents must coordinate under highly dynamic interactions, where s…
R2PS: Worst-Case Robust Real-Time Pursuit Strategies under Partial Observability
Runyu Lu, Ruochuan Shi, Yuanheng Zhu +1
Computing worst-case robust strategies in pursuit-evasion games (PEGs) is time-consuming, especially when real-world factors like partial observability are considered. While import…
Empowering Multi-Robot Cooperation via Sequential World Models
Zijie Zhao, Honglei Guo, Shengqian Chen +4
Model-based reinforcement learning (MBRL) has achieved remarkable success in robotics due to its high sample efficiency and planning capability. However, extending MBRL to physical…
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Yuqian Fu, Tinghong Chen, Jiajun Chai +7
Large language models (LLMs) have achieved remarkable progress in reasoning tasks, yet the optimal integration of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) remai…
DipLLM: Fine-Tuning LLM for Strategic Decision-making in Diplomacy
Kaixuan Xu, Jiajun Chai, Sicheng Li +3
Diplomacy is a complex multiplayer game that requires both cooperation and competition, posing significant challenges for AI systems. Traditional methods rely on equilibrium search…
RLAE: Reinforcement Learning-Assisted Ensemble for LLMs
Yuqian Fu, Yuanheng Zhu, Jiajun Chai +4
Ensembling large language models (LLMs) can effectively combine diverse strengths of different models, offering a promising approach to enhance performance across various tasks. Ho…
Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement
Zhi Wang, Li Zhang, Wenhao Wu +3
A longstanding goal of artificial general intelligence is highly capable generalists that can learn from diverse experiences and generalize to unseen tasks. The language and vision…
Discretizing Continuous Action Space with Unimodal Probability Distributions for On-Policy Reinforcement Learning
Yuanyang Zhu, Zhi Wang, Yuanheng Zhu +2
For on-policy reinforcement learning, discretizing action space for continuous control can easily express multiple modes and is straightforward to optimize. However, without consid…
FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game
Guangzheng Hu, Yuanheng Zhu, Haoran Li +1
Many real-world applications involve some agents that fall into two teams, with payoffs that are equal within the same team but of opposite sign across the opponent team. The so-ca…
Score-Based Equilibrium Learning in Multi-Player Finite Games with Imperfect Information
Runyu Lu, Yuanheng Zhu, Dongbin Zhao
Real-world games, which concern imperfect information, multiple players, and simultaneous moves, are less frequently discussed in the existing literature of game theory. While rein…
NeuronsMAE: A Novel Multi-Agent Reinforcement Learning Environment for Cooperative and Competitive Multi-Robot Tasks
Guangzheng Hu, Haoran Li, Shasha Liu +3
Multi-agent reinforcement learning (MARL) has achieved remarkable success in various challenging problems. Meanwhile, more and more benchmarks have emerged and provided some standa…
A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combat
Jiajun Chai, Wenzhang Chen, Yuanheng Zhu +2
Unmanned combat air vehicle (UCAV) combat is a challenging scenario with continuous action space. In this paper, we propose a general hierarchical framework to resolve the within-v…
NVIF: Neighboring Variational Information Flow for Large-Scale Cooperative Multi-Agent Scenarios
Jiajun Chai, Yuanheng Zhu, Dongbin Zhao
Communication-based multi-agent reinforcement learning (MARL) provides information exchange between agents, which promotes the cooperation. However, existing methods cannot perform…
UNMAS: Multi-Agent Reinforcement Learning for Unshaped Cooperative Scenarios
Jiajun Chai, Weifan Li, Yuanheng Zhu +4
Multi-agent reinforcement learning methods such as VDN, QMIX, and QTRAN that adopt centralized training with decentralized execution (CTDE) framework have shown promising results i…
Empirical Policy Optimization for -Player Markov Games
Yuanheng Zhu, Dongbin Zhao, Mengchen Zhao +1
In single-agent Markov decision processes, an agent can optimize its policy based on the interaction with environment. In multi-player Markov games (MGs), however, the interaction…
Event-Triggered Multi-agent Reinforcement Learning with Communication under Limited-bandwidth Constraint
Guangzheng Hu, Yuanheng Zhu, Dongbin Zhao +2
Communicating with each other in a distributed manner and behaving as a group are essential in multi-agent reinforcement learning. However, real-world multi-agent systems suffer fr…
Enhanced Rolling Horizon Evolution Algorithm with Opponent Model Learning: Results for the Fighting Game AI Competition
Zhentao Tang, Yuanheng Zhu, Dongbin Zhao +1
The Fighting Game AI Competition (FTGAIC) provides a challenging benchmark for 2-player video game AI. The challenge arises from the large action space, diverse styles of character…
A Survey of Deep Reinforcement Learning in Video Games
Kun Shao, Zhentao Tang, Yuanheng Zhu +2
Deep reinforcement learning (DRL) has made great achievements since proposed. Generally, DRL agents receive high-dimensional inputs at each step, and make actions according to deep…
StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning
Kun Shao, Yuanheng Zhu, Dongbin Zhao
Real-time strategy games have been an important field of game artificial intelligence in recent years. This paper presents a reinforcement learning and curriculum transfer learning…