Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi

Published in

(2023)

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TL;DR

This work formally defined a framework based on a popular cooperative multi-agent game called Hanabi to evaluate the adaptability of MARL methods and defined a new metric called adaptation regret that measures the agent's ability to efficiently adapt and improve its coordination performance when paired with some held-out pool of partners on top of its ZSC performance.

Abstract

Cooperative Multi-agent Reinforcement Learning (MARL) algorithms with Zero-Shot Coordination (ZSC) have gained significant attention in recent years. ZSC refers to the ability of agents to coordinate zero-shot (without additional interaction experience) with independently trained agents. While ZSC is crucial for cooperative MARL agents, it might not be possible for complex tasks and changing environments. Agents also need to adapt and improve their performance with minimal interaction with other agents. In this work, we show empirically that state-of-the-art ZSC algorithms have poor performance when paired with agents trained with different learning methods, and they require millions of interaction samples to adapt to these new partners. To investigate this issue, we formally defined a framework based on a popular cooperative multi-agent game called Hanabi to evaluate the adaptability of MARL methods. In particular, we created a diverse set of pre-trained agents and defined a new metric called adaptation regret that measures the agent's ability to efficiently adapt and improve its coordination performance when paired with some held-out pool of partners on top of its ZSC performance. After evaluating several SOTA algorithms using our framework, our experiments reveal that naive Independent Q-Learning (IQL) agents in most cases adapt as quickly as the SOTA ZSC algorithm Off-Belief Learning (OBL). This finding raises an interesting research question: How to design MARL algorithms with high ZSC performance and capability of fast adaptation to unseen partners. As a first step, we studied the role of different hyper-parameters and design choices on the adaptability of current MARL algorithms. Our experiments show that two categories of hyper-parameters controlling the training data diversity and optimization process have a significant impact on the adaptability of Hanabi agents.

Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi

Published in

(2023)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Sarath Chandar

3 papers

Janarthanan Rajendran

3 papers

Hadi Nekoei

1 papers

Xutong Zhao

1 papers

Miao Liu

1 papers

References37 items

K-level Reasoning for Zero-Shot Coordination in Hanabi

On-the-fly Strategy Adaptation for ad-hoc Agent Coordination

Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination

Deep Reinforcement Learning at the Edge of the Statistical Precipice

Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi

TL;DR

Abstract

TL;DR

Abstract

Authors

References37 items

K-level Reasoning for Zero-Shot Coordination in Hanabi

On-the-fly Strategy Adaptation for ad-hoc Agent Coordination

Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination

Deep Reinforcement Learning at the Edge of the Statistical Precipice

Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi

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