Opponent Modeling

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Hierarchical Belief Modeling for Zero-Shot Opponent Adaptation in Partially Observable Multi-Agent Navigation
Sep 14, 2026Kowei Shih, Lu Cheng, Zeyu Wang +2Opponent Modeling Partially Observable RL
Tsinghua University, Beijing, China · Stevens Institute of Technology, New Jersey, USA · University of California, Los Angeles, Los Angeles, USA +2
RevengeBench: Reverse Engineering Code-Space Policies from Behavioral Experiments
Jun 24, 2026Babak Rahmani, Sebastian Dziadzio, Joschka Strüber +2Opponent Modeling LLM-Based Program Synthesis
Tübingen AI Center
Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents
Jun 16, 2026Ankita Samaddar, Sandeep Neema, Daniel Balasubramanian +1Opponent Modeling Autonomous Cyber Defense
Departmaent of Computer Science Vanderbilt University Nashville, TN
Enhancing the MADDPG Algorithm for Multi-Agent Learning via Action Inference and Importance Sampling
Jun 3, 2026Marc Walden, Jason Liu, Shaashwath Sivakumar +2Opponent Modeling Importance Sampling
Department of Mathematics, University of California Los Angeles, Los Angeles, CA, USA
Generalized Intention Modeling in Multi-Agent Reinforcement Learning
May 29, 2026Mateusz Odrowaz-Sypniewski, Jasmine Bayrooti, Ajay Shankar +1Opponent Modeling Multi-Agent Reasoning
Department of Computer Science and Technology, University of Cambridge, UK
AlphaExploitem: Going Beyond the Nash Equilibrium in Poker by Learning to Exploit Suboptimal Play
May 9, 2026Vlad Murgoci, Matthijs Spaan, Yaniv OrenOpponent Modeling Game-Playing Agents
Delft University of Technology · CD Delft, The Netherlands · Department of Intelligent Systems
SOM: Structured Opponent Modeling for LLM-based Agents via Structural Causal Model
May 8, 2026Shiyue Cao, Pei Xu, Likun Yang +3Opponent Modeling Multi-Agent Reasoning
School of Artificial Intelligence, University of Chinese Academy of Sciences & Institute of Automation, Chinese Academy of Sciences Beijing, China · National Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences Beijing, China +1
Distilling Bayesian Belief States into Language Models for Auditable Negotiation
May 6, 2026Zongqi Cui, Baihan LinMulti-Agent Negotiation Opponent Modeling
Emory University · Icahn School of Medicine at Mount Sinai
StratFormer: Adaptive Opponent Modeling and Exploitation in Imperfect-Information Games
Apr 28, 2026Andy Caen, Mark H. M. Winands, Dennis J. N. J. SoemersOpponent Modeling Game-Playing Agents
Department of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
Preference Estimation via Opponent Modeling in Multi-Agent Negotiation
Apr 17, 2026Yuta Konishi, Kento Yamamoto, Eisuke Sonomoto +8Multi-Agent Negotiation Opponent Modeling
1Graduate School of Informatics, Kyoto University, Kyoto, Japan · 2Accenture Japan Ltd, Tokyo, Japan

Sep 14, 2026, cs.AIJ/K move · Enter open · S save

Hierarchical Belief Modeling for Zero-Shot Opponent Adaptation in Partially Observable Multi-Agent Navigation

Kowei Shih, Lu Cheng, Zeyu Wang, Yeyun Xu+1

Tsinghua University, Beijing, China · Stevens Institute of Technology, New Jersey, USA · University of California, Los Angeles, Los Angeles, USA+2

Opponent Modeling Partially Observable RL Hierarchical RL Multi-Agent Path Finding

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Lux AI Season 3 requires agents to act under partial observability, randomized episode level dynamics, and a best of five match structure that rewards both tactical execution and fast adaptation. We present HORIZON, a hierarchical agent that combines symmetry aware spatial perception, dual memory belief tracking, relic centric graph attention, information gain driven exploration, and an opponent conditioned policy mixture. HORIZON separates short horizon control from cross match meta reasoning, while auxiliary belief and world model objectives stabilize learning. Trained with PPO in a large scale JAX simulator, the resulting agent explicitly infers hidden game parameters and opponent style. Experiments show consistent gains in match win rate, episode win rate, adaptation gain, and league rating over strong recurrent and feed forward baselines.

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