Adaptively Coordinating with Novel Partners via Learned Latent Strategies

AI summary

Overview

Research area: multi-agent reinforcement learning, human-agent collaboration, zero-shot coordination, and ad hoc teamwork. Technical level: Advanced. Scope: Introduces TALENTS, a strategy-conditioned cooperator that learns latent partner strategies from population data and uses no-regret online adaptation to coordinate with novel human and agent teammates in a modified Overcooked environment.

What This Paper Is About

AI agents often need to collaborate with unfamiliar partners whose strategies vary and can change during an episode. This paper develops a method that infers a partner's strategy type in real time and selects complementary actions, without having trained with that specific partner before.

Key Contributions

1. A variational autoencoder (VAE) learns a latent strategy space from diverse agent trajectories, and K-means with silhouette analysis clusters this space into discrete strategy types.
2. A strategy-conditioned cooperator is trained to produce best responses for each strategy cluster by generating synthetic partners from that cluster during training.
3. An online fixed-share regret minimization algorithm infers a novel partner's strategy from observed actions and enables intra-episodic adaptation when the partner changes behavior.
4. The method achieves state-of-the-art results in modified Overcooked against GAMMA and population best-response baselines, including a human subject study with 119 participants.

Main Findings

• Agent-agent superiority: TALENTS achieved the highest average reward on Open, Hallway, and Ring layouts across FCP, MEP, and BP populations. The exception was Forced Coordination, where the population best-response baseline performed better due to clear role division and sparse rewards.
• Fixed-share adaptation matters: Replacing fixed-share with a static-regret minimizer caused the agent to fail after a partner switched policies midway through an episode. Fixed-share tracked the change and maintained performance.
• Human-agent performance: In an online user study, TALENTS significantly outperformed GAMMA and the population best-response baseline on team score (F(2,166)=5.76, p=.003). It also received higher team fluency (F(2,122)=4.31, p=.02) and trust ratings than the population best-response baseline.
• Population robustness: Benefits held whether trained with FCP, MEP, or BP populations. BP-trained TALENTS produced the best absolute scores.
• Key limitations: The agent struggles in Forced Coordination due to sparse reward signals with low-skill partners. Generalization is bounded by the VAE's interpolation ability. Slowing agent actions to match human action rates may limit long-term planning.

Methodology in Plain English

The researchers collected trajectories from many pre-trained agents. A VAE compresses each agent's behavior into a latent vector that predicts future actions. K-means with silhouette analysis groups these vectors into strategy types. During training, the cooperator samples a strategy cluster, decodes it into a synthetic partner, and learns to best respond to that type using a bias vector that adjusts its action preferences. At test time, each cluster acts as an expert predicting the partner's next action. The agent compares predictions to actual partner actions, updates expert weights via fixed-share, and conditions its policy on the leading expert. This allows it to switch its belief mid-episode if the partner changes strategy. Evaluation used a modified Overcooked domain with order timers and bonus rewards, followed by a human study.

Why This Matters

Impact on research: This work bridges population-based training and online agent modeling. It shows that agents can adapt to humans without human training data, advancing ad hoc teamwork and zero-shot coordination.

Real-world applications:

• Collaborative robots in manufacturing or warehouses that adapt to different human co-workers' pace and preferences.
• Assistive robots in healthcare or elder care that adjust to patient routines and changing needs.
• Human-AI teams in disaster response or search-and-rescue where partners and conditions shift unpredictably.
• Interactive game AI or personalized tutoring systems that coordinate with diverse users.

Industry relevance: The method reduces the need to retrain for every teammate and supports deployable human-robot teams. It is relevant to robotics, autonomous systems, human-robot interaction, and multi-agent AI.

Future Directions

• Improve Forced Coordination and sparse-reward performance through better partner sampling, reward shaping, or curriculum learning.
• Extend VAE generalization to out-of-distribution partners, possibly by incorporating human data or adaptive latent spaces.
• Address real-time constraints and long-term planning when matching human action rates.
• Test in more domains with richer communication, more agents, and physical robots. Explore change-point detection or hierarchical regret for complex non-stationarity.

Target Audience

Researchers and graduate students in multi-agent reinforcement learning, human-robot interaction, zero-shot coordination, and game AI. Also useful for practitioners building collaborative AI systems and HCI researchers studying trust, fluency, and workload in human-agent teams.

Authors’ abstract

Adaptation is the cornerstone of effective collaboration among heterogeneous team members. In human-agent teams, artificial agents need to adapt to their human partners in real time, as individuals often have unique preferences and policies that may change dynamically throughout interactions. This becomes particularly challenging in tasks with time pressure and complex strategic spaces, where identifying partner behaviors and selecting suitable responses is difficult. In this work, we introduce a strategy-conditioned cooperator framework that learns to represent, categorize, and adapt to a broad range of potential partner strategies in real-time. Our approach encodes strategies with a variational autoencoder to learn a latent strategy space from agent trajectory data, identifies distinct strategy types through clustering, and trains a cooperator agent conditioned on these clusters by generating partners of each strategy type. For online adaptation to novel partners, we leverage a fixed-share regret minimization algorithm that dynamically infers and adjusts the partner's strategy estimation during interaction. We evaluate our method in a modified version of the Overcooked domain, a complex collaborative cooking environment that requires effective coordination among two players with a diverse potential strategy space. Through these experiments and an online user study, we demonstrate that our proposed agent achieves state of the art performance compared to existing baselines when paired with novel human, and agent teammates.