# Modeling Latent Partner Strategies for Adaptive Zero-Shot Human-Agent Collaboration

arXiv: https://arxiv.org/abs/2507.05244 · HTML: https://mlsanity.com/paper/2507.05244/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 40 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 22 of 22)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?](https://mlsanity.com/paper/2011.09533/) (2020)
- [Human-level performance in first-person multiplayer games with population-based deep reinforcement learning](https://mlsanity.com/paper/1807.01281/) (2018)
- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- [Learning with Opponent-Learning Awareness](https://mlsanity.com/paper/1709.04326/) (2017)
- ["Other-Play" for Zero-Shot Coordination](https://mlsanity.com/paper/2003.02979/) (2020)
- [Collaborating with Humans without Human Data](https://mlsanity.com/paper/2110.08176/) (2021)
- [Theory of Mind for Multi-Agent Collaboration via Large Language Models](https://mlsanity.com/paper/2310.10701/) (2023)
- [Opponent Modeling in Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05559/) (2016)
- [Learning Latent Representations to Influence Multi-Agent Interaction](https://mlsanity.com/paper/2011.06619/) (2020)
- [Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations](https://mlsanity.com/paper/1308.3513/) (2013)
- [Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination](https://mlsanity.com/paper/2112.11701/) (2021)
- [Modeling Others using Oneself in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1802.09640/) (2018)
- [Learning Policy Representations in Multiagent Systems](https://mlsanity.com/paper/1806.06464/) (2018)
- [Efficient Model Learning for Human-Robot Collaborative Tasks](https://mlsanity.com/paper/1405.6341/) (2014)
- [Deep Interactive Bayesian Reinforcement Learning via Meta-Learning](https://mlsanity.com/paper/2101.03864/) (2021)
- [ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordination](https://mlsanity.com/paper/2310.05208/) (2023)
- [Learning to Cooperate with Humans using Generative Agents](https://mlsanity.com/paper/2411.13934/) (2024)
- [Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2307.01158/) (2023)
- [Variational Autoencoders for Opponent Modeling in Multi-Agent Systems](https://mlsanity.com/paper/2001.10829/) (2020)
- [Coordination with Humans via Strategy Matching](https://mlsanity.com/paper/2210.15099/) (2022)

## Built on by (most cited first, 1 of 1)

- [Implicit Neural Representations of Individual Behavior](https://mlsanity.com/paper/2606.12200/) (2026)

## Abstract (arXiv)

In collaborative tasks, being able to adapt to your teammates is a necessary requirement for success. When teammates are heterogeneous, such as in human-agent teams, agents need to be able to observe, recognize, and adapt to their human partners in real time. This becomes particularly challenging in tasks with time pressure and complex strategic spaces where the dynamics can change rapidly. In this work, we introduce TALENTS, a strategy-conditioned cooperator framework that learns to represent, categorize, and adapt to a range of partner strategies, enabling ad-hoc teamwork. Our approach utilizes a variational autoencoder to learn a latent strategy space from trajectory data. This latent space represents the underlying strategies that agents employ. Subsequently, the system identifies different types of strategy by clustering the data. Finally, a cooperator agent is trained to generate partners for each type of strategy, conditioned on these clusters. In order to adapt to previously unseen partners, we leverage a fixed-share regret minimization algorithm that infers and adjusts the estimated partner strategy dynamically. We assess our approach in a customized version of the Overcooked environment, posing a challenging cooperative cooking task that demands strong coordination across a wide range of possible strategies. Using an online user study, we show that our agent outperforms current baselines when working with unfamiliar human partners.