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Equivariant Policies for Robust Zero-Shot Coordination in Decentralized Partially Observable Markov Decision Processes


Core Concepts
Equivariant network architectures can effectively leverage environmental symmetry to improve zero-shot coordination between independently trained agents in decentralized partially observable Markov decision processes.
Abstract
The paper presents a novel equivariant network architecture, called the Equivariant Coordinator (EQC), for use in decentralized partially observable Markov decision processes (Dec-POMDPs) to improve zero-shot coordination between independently trained agents. Key highlights: EQC mathematically guarantees symmetry-equivariance of multi-agent policies, and can be applied solely at test time as a coordination-improvement operator. EQC outperforms prior symmetry-robust baselines on the AI benchmark Hanabi. EQC can be used to improve the coordination ability of a variety of pre-trained policies, including the state-of-the-art for zero-shot coordination on Hanabi. The authors provide theoretical guarantees for the properties of EQC and demonstrate its empirical efficacy through extensive experiments. The paper first formalizes the problem setting of Dec-POMDPs and introduces relevant group-theoretic concepts like equivariance. It then presents the EQC architecture and discusses its mathematical properties. The authors propose two algorithmic approaches using EQC: 1) training agents with a group-based Other-Play learning rule, then symmetrizing them at test time, and 2) symmetrizing pre-trained agents at test time. The experiments on the Hanabi benchmark show that both approaches outperform prior symmetry-aware baselines in zero-shot coordination. The authors also demonstrate that EQC can be used as a coordination-improvement operator to enhance the performance of diverse pre-trained policies, including the state-of-the-art for zero-shot coordination on Hanabi.
Stats
The paper does not contain any explicit numerical data or statistics. The key results are presented in the form of comparative performance metrics between different agent types on the Hanabi benchmark.
Quotes
"Equivariant policies are such that symmetric changes to their observation cause a corresponding change to their output. In doing so, we fundamentally prevent the agent from breaking symmetries over the course of training." "Our method also acts as a "coordination-improvement operator" for generic, pre-trained policies, and thus may be applied at test-time in conjunction with any self-play algorithm." "We provide theoretical guarantees of our work and test on the AI benchmark task of Hanabi, where we demonstrate our methods outperforming other symmetry-aware baselines in zero-shot coordination, as well as able to improve the coordination ability of a variety of pre-trained policies."

Key Insights Distilled From

by Darius Mugli... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2210.12124.pdf
Equivariant Networks for Zero-Shot Coordination

Deeper Inquiries

How can the choice of the group G for G-equivariant agents be optimized for a given task and policy type to maximize zero-shot coordination performance

To optimize the choice of the group G for G-equivariant agents in order to maximize zero-shot coordination performance, several factors need to be considered. Firstly, the group G should be selected based on the specific symmetries present in the environment relevant to the task at hand. Understanding the underlying symmetries of the domain can help in choosing a group that captures these symmetries effectively. For instance, in the context of the Hanabi game, the group G was chosen to represent the permutations of card colors, as these symmetries were crucial for coordination. Secondly, the group G should be tailored to the policy type being used. Different policies may exhibit varying degrees of symmetry or regularity in their behavior across different permutations. By aligning the group G with the characteristics of the policy type, it is possible to ensure that the equivariant modeling approach is well-suited to the specific learning dynamics of the agents. Moreover, the size and complexity of the group G should be considered. While larger groups may capture more symmetries, they can also lead to increased computational complexity. Balancing the richness of symmetries captured by G with the practical constraints of training and inference is essential for optimizing performance. In practice, a systematic exploration of different group structures, possibly through hyperparameter tuning or automated search algorithms, can help identify the optimal choice of G for a given task and policy type. By iteratively testing and evaluating the performance of G-equivariant agents with different group configurations, researchers can fine-tune the selection to achieve the best zero-shot coordination outcomes.

What techniques can be used to efficiently uncover the environmental symmetries of a domain when they are not given or assumed to be known

Efficiently uncovering environmental symmetries in a domain when they are not given or assumed to be known requires a combination of data-driven exploration and algorithmic techniques. One approach is to leverage unsupervised learning methods, such as clustering and dimensionality reduction, to identify patterns and regularities in the data that may correspond to underlying symmetries. Additionally, techniques from reinforcement learning, such as intrinsic motivation and curiosity-driven exploration, can be employed to encourage agents to actively seek out and exploit symmetries in the environment. By rewarding agents for discovering and leveraging symmetries, they can autonomously uncover latent structures that contribute to improved coordination. Furthermore, meta-learning algorithms can be utilized to adapt the agent's exploration strategy based on past experiences and feedback. By learning how to efficiently explore and exploit symmetries over time, agents can progressively uncover and utilize environmental regularities to enhance their coordination abilities. Overall, a combination of machine learning, reinforcement learning, and meta-learning techniques can be harnessed to efficiently uncover environmental symmetries in a domain, even when they are not explicitly provided, enabling agents to adapt and coordinate effectively in complex and unknown environments.

How can the principles of equivariant modeling be extended beyond coordination to other fundamental aspects of multi-agent cooperation, such as credit assignment and exploration

The principles of equivariant modeling can be extended beyond coordination to other fundamental aspects of multi-agent cooperation, such as credit assignment and exploration, by incorporating symmetry-aware techniques into the design of learning algorithms. For credit assignment, equivariant modeling can help ensure that rewards and feedback are appropriately attributed to the actions of individual agents in a coordinated system. By enforcing symmetry constraints in the credit assignment process, agents can receive fair and consistent feedback based on their contributions to the collective task. In terms of exploration, equivariant modeling can guide agents to explore the environment in a structured and systematic manner that respects the underlying symmetries. By encouraging agents to explore in ways that preserve symmetry, they can discover new strategies and behaviors that lead to improved coordination and performance. Moreover, equivariant modeling can facilitate the transfer of knowledge and policies between agents in a multi-agent system. By ensuring that policies are invariant or equivariant to symmetries, agents can more effectively learn from each other and adapt their behaviors based on shared experiences, leading to enhanced cooperation and coordination. By integrating equivariant modeling principles into credit assignment, exploration, and knowledge transfer processes, multi-agent systems can achieve higher levels of coordination, efficiency, and adaptability in complex and dynamic environments.
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