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Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement Learning (2302.00521v2)

Published 1 Feb 2023 in cs.LG, cs.AI, and cs.MA

Abstract: Being able to harness the power of large datasets for developing cooperative multi-agent controllers promises to unlock enormous value for real-world applications. Many important industrial systems are multi-agent in nature and are difficult to model using bespoke simulators. However, in industry, distributed processes can often be recorded during operation, and large quantities of demonstrative data stored. Offline multi-agent reinforcement learning (MARL) provides a promising paradigm for building effective decentralised controllers from such datasets. However, offline MARL is still in its infancy and therefore lacks standardised benchmark datasets and baselines typically found in more mature subfields of reinforcement learning (RL). These deficiencies make it difficult for the community to sensibly measure progress. In this work, we aim to fill this gap by releasing off-the-grid MARL (OG-MARL): a growing repository of high-quality datasets with baselines for cooperative offline MARL research. Our datasets provide settings that are characteristic of real-world systems, including complex environment dynamics, heterogeneous agents, non-stationarity, many agents, partial observability, suboptimality, sparse rewards and demonstrated coordination. For each setting, we provide a range of different dataset types (e.g. Good, Medium, Poor, and Replay) and profile the composition of experiences for each dataset. We hope that OG-MARL will serve the community as a reliable source of datasets and help drive progress, while also providing an accessible entry point for researchers new to the field.

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Summary

  • The paper introduces OG-MARL as a standardized repository for offline multi-agent RL, offering diverse datasets categorized by behavior policy performance.
  • It implements baseline algorithms, including adaptations like QMIX+CQL, to evaluate offline MARL performance in complex, pixel-based environments.
  • The study provides actionable insights for advancing research in cooperative, heterogeneous, and non-stationary multi-agent systems.

Overview of "Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement Learning"

The paper "Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement Learning" addresses a significant gap in the current research landscape of offline multi-agent reinforcement learning (MARL). As offline MARL is still an emerging area, there is a dearth of standardized datasets and baselines that are essential for assessing research progress effectively. To bridge this gap, the paper introduces the Off-the-Grid MARL (OG-MARL), a comprehensive repository of high-quality datasets accompanied by baseline implementations tailored for cooperative offline MARL scenarios.

Dataset Characteristics and Methodology

OG-MARL is specifically crafted to encompass a wide range of real-world characteristics and complexities inherent in multi-agent systems, such as heterogeneous agents, non-stationarity, partial observability, and varying levels of environment complexity. The dataset compilation covers diverse behavior policies, including independent learners and centralised training paradigms. This expansive approach aims to provide a robust experimental framework that can evaluate offline MARL algorithms under realistic conditions.

An important aspect of OG-MARL is the categorization of datasets into Good, Medium, Poor, and Replay based on the performance of the behavior policies generating them. The datasets are profiled to provide a statistical composition, offering insights into episode return distributions through visualizations like violin plots.

Baselines and Evaluation

The authors employed a range of state-of-the-art offline MARL algorithms as baselines, notably adapting the classical algorithms augmented with strategies like conservative value regularization (as in CQL) and policy constraints (such as BCQ). These algorithms include MAICQ and novel adaptations like QMIX+CQL, which provide a spectrum of techniques addressing the extrapolation error and other challenges prevalent in offline MARL.

One of the critical contributions of the paper is the performance benchmarking on new environments with pixel-based observations, such as PettingZoo's Pursuit and Co-op Pong, extending the challenge dimensions beyond traditional environments. This evaluation illustrates the readiness and applicability of offline MARL techniques to handle complex, high-dimensional observation spaces.

Implications and Future Directions

The release of OG-MARL is an imperative stride toward standardizing research in offline MARL. By providing both datasets and baseline implementations, the repository acts as a pivotal resource that enables researchers to benchmark their developments and compare novel algorithms on consistent grounds. It lays a significant foundation for accelerating advancements in applying MARL to real-world problems, emphasizing domains with distributed, cooperative, and competitive agent interactions.

For future developments, expanding the repository to include datasets derived from non-RL sources such as human operators or handcrafted controllers might be insightful. Additionally, extending the research to competitive settings can further broaden the applicability of offline MARL.

In conclusion, "Off-the-Grid MARL" provides a cornerstone for the systematic advancement of offline multi-agent reinforcement learning, offering valuable tools and data for the research community to build upon. The continued development and augmentation of the OG-MARL repository hold the potential to drive substantial progress in the application of MARL techniques, fostering collaborative and competitive learning systems that align closely with real-world scenarios.

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