Publications (21)
L2E: Learning to Exploit Your Opponent
Zhe Wu, Kai Li, Enmin Zhao +5
Opponent modeling is essential to exploit sub-optimal opponents in strategic interactions. Most previous works focus on building explicit models to directly predict the opponents'…
Policy Space Diversity for Non-Transitive Games
Jian Yao, Weiming Liu, Haobo Fu +4
Policy-Space Response Oracles (PSRO) is an influential algorithm framework for approximating a Nash Equilibrium (NE) in multi-agent non-transitive games. Many previous studies have…
Maximum Entropy Heterogeneous-Agent Reinforcement Learning
Jiarong Liu, Yifan Zhong, Siyi Hu +4
Multi-agent reinforcement learning (MARL) has been shown effective for cooperative games in recent years. However, existing state-of-the-art methods face challenges related to samp…
Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning
Hanlin Yang, Jian Yao, Weiming Liu +13
Recovering a spectrum of diverse policies from a set of expert trajectories is an important research topic in imitation learning. After determining a latent style for a trajectory,…
Deep (Predictive) Discounted Counterfactual Regret Minimization
Hang Xu, Kai Li, Haobo Fu +3
Counterfactual regret minimization (CFR) is a family of algorithms for effectively solving imperfect-information games. To enhance CFR's applicability in large games, researchers u…
Minimizing Weighted Counterfactual Regret with Optimistic Online Mirror Descent
Hang Xu, Kai Li, Bingyun Liu +4
Counterfactual regret minimization (CFR) is a family of algorithms for effectively solving imperfect-information games. It decomposes the total regret into counterfactual regrets,…
Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space
Jiechao Xiong, Qing Wang, Zhuoran Yang +7
Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely. Motivated by applications in computer games, we…
Goal-Oriented Skill Abstraction for Offline Multi-Task Reinforcement Learning
Jinmin He, Kai Li, Yifan Zang +4
Offline multi-task reinforcement learning aims to learn a unified policy capable of solving multiple tasks using only pre-collected task-mixed datasets, without requiring any onlin…
TacticCraft: Natural Language-Driven Tactical Adaptation for StarCraft II
Weiyu Ma, Jiwen Jiang, Haobo Fu +1
We present an adapter-based approach for tactical conditioning of StarCraft II AI agents. Current agents, while powerful, lack the ability to adapt their strategies based on high-l…
Reaching Consensus in Cooperative Multi-Agent Reinforcement Learning with Goal Imagination
Liangzhou Wang, Kaiwen Zhu, Fengming Zhu +6
Reaching consensus is key to multi-agent coordination. To accomplish a cooperative task, agents need to coherently select optimal joint actions to maximize the team reward. However…
AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization
Shengda Gu, Kai Li, Xinyi Ke +3
AutoPref uses a large language model to automatically discover and compose pairwise loss and weighting programs that define preference objectives for neural combinatorial optimizat…
Enhance Reasoning for Large Language Models in the Game Werewolf
Shuang Wu, Liwen Zhu, Tao Yang +4
This paper presents an innovative framework that integrates Large Language Models (LLMs) with an external Thinker module to enhance the reasoning capabilities of LLM-based agents.…
Enhanced Equilibria-Solving via Private Information Pre-Branch Structure in Adversarial Team Games
Chen Qiu, Haobo Fu, Kai Li +3
In ex ante coordinated adversarial team games (ATGs), a team competes against an adversary, and the team members are only allowed to coordinate their strategies before the game sta…
AI Deception: Risks, Dynamics, and Controls
Boyuan Chen, Sitong Fang, Jiaming Ji +56
As intelligence increases, so does its shadow. AI deception, in which systems induce false beliefs to secure self-beneficial outcomes, has evolved from a speculative concern to an…
Diversity from Human Feedback
Ren-Jian Wang, Ke Xue, Yutong Wang +4
Diversity plays a significant role in many problems, such as ensemble learning, reinforcement learning, and combinatorial optimization. How to define the diversity measure is a lon…
Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance
Jinmin He, Kai Li, Yifan Zang +4
Multi-task reinforcement learning endeavors to efficiently leverage shared information across various tasks, facilitating the simultaneous learning of multiple tasks. Existing appr…
Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution
Ke Xue, Yutong Wang, Cong Guan +5
Generating agents that can achieve zero-shot coordination (ZSC) with unseen partners is a new challenge in cooperative multi-agent reinforcement learning (MARL). Recently, some stu…
Pointer Networks Trained Better via Evolutionary Algorithms
Muyao Zhong, Shengcai Liu, Bingdong Li +3
Pointer Network (PtrNet) is a specific neural network for solving Combinatorial Optimization Problems (COPs). While PtrNets offer real-time feed-forward inference for complex COPs…
Preference Goal Tuning: Post-Training as Latent Control for Frozen Policies
Guangyu Zhao, Kewei Lian, Haoxuan Ru +8
Goal-conditioned policies enable decision-making models to execute diverse behaviors based on specified goals, yet their downstream performance is often highly sensitive to the cho…
SeeNav-Agent: Enhancing Vision-Language Navigation with Visual Prompt and Step-Level Policy Optimization
Zhengcheng Wang, Zichuan Lin, Yijun Yang +2
Existing Vision-Language Navigation (VLN) agents based on Large Vision-Language Models (LVLMs) often suffer from perception errors, reasoning errors, and planning errors, which sig…
Not All Tasks Are Equally Difficult: Multi-Task Deep Reinforcement Learning with Dynamic Depth Routing
Jinmin He, Kai Li, Yifan Zang +4
Multi-task reinforcement learning endeavors to accomplish a set of different tasks with a single policy. To enhance data efficiency by sharing parameters across multiple tasks, a c…