Mark Crowley

recent highlighted publications

1. Multi Type Mean Field Reinforcement Learning

   Sriram Ganapathi Subramanian, Pascal Poupart, Matthew Taylor, and Nidhi Hegde.

   In Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) International Foundation for Autonomous Agents and Multiagent Systems, London, United Kingdom, 2020.

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2. Upcoming Book

   Elements of Dimensionality Reduction and Manifold Learning

   Benyamin Ghojogh, Mark Crowley, Fakhri Karray, and Ali Ghodsi.

   Springer Nature, dec, 2022.

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   Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data, for better representation of data or separation between classes. This book presents a cohesive review of linear and nonlinear dimensionality reduction and manifold learning. Three main aspects of dimensionality reduction are covered – spectral dimensionality reduction, probabilistic dimensionality reduction, and neural network-based dimensionality reduction, which have geometric, probabilistic, and information-theoretic points of view to dimensionality reduction, respectively. This book delves into basic concepts and recent developments in the field of dimensionality reduction and manifold learning, providing the reader with a comprehensive understanding. The necessary background and preliminaries, on linear algebra, optimization, and kernels, are also explained in the book to ensure a comprehensive understanding of the algorithms. The tools this book introduces are applied to various applications involving feature extraction, image processing, computer vision, and signal processing. This book is applicable to a wide audience who would like to acquire a deep understanding of the various ways to extract, transform, and understand the structure of data. The intended audiences are academics, students, and industry professionals. Academic researchers and students can use this book as a textbook for machine learning and dimensionality reduction. Data scientists, machine learning scientists, computer vision scientists, computer scientists, statisticians, and mathematicians in the fields of applied mathematics, statistical learning, Riemannian manifolds, subspace analysis, linear algebra, and optimization can use this book as a reference book for both technical and applied concepts. Industry professionals, including applied engineers, data engineers, and engineers in various fields of science dealing with machine learning, can use this book as a guidebook for feature extraction from their data as the raw data in industry often requires pre-processing. Data feature extraction is useful in various fields of science including engineering, physics, chemistry, biometrics, biomedical signals and images, etc. This book is structured as a reference textbook, so that it can be used for advanced courses, as an in-depth supplementary resource or for researchers or practitioners who want to learn about dimensionality reduction and manifold learning. The book is grounded in theory, but provides thorough explanations and diverse examples to improve the readers comprehension of the advanced topics. Advanced methods are explained in a step-by-step manner so that readers of all levels can follow the reasoning and come to a deep understanding of the concepts. This book does not assume the reader has an advanced theoretical background in machine learning and provides necessary background, though an undergraduate-level background in linear algebra and calculus is recommended.

3. Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning

   Sriram Ganapathi Subramanian, Matthew Taylor, Kate Larson, and Mark Crowley

   In Proceedings of the 22nd International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), London, United Kingdom, sep, 2023.

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   Multi-agent reinforcement learning typically suffers from the problem of sample inefficiency, where learning suitable policies involves the use of many data samples. Learning from external demonstrators is a possible solution that mitigates this problem. However, most prior approaches in this area assume the presence of a single demonstrator. Leveraging multiple knowledge sources (i.e., advisors) with expertise in distinct aspects of the environment could substantially speed up learning in complex environments. This paper considers the problem of simultaneously learning from multiple independent advisors in multi-agent reinforcement learning. The approach leverages a two-level Q-learning architecture, and extends this framework from single-agent to multi-agent settings. We provide principled algorithms that incorporate a set of advisors by both evaluating the advisors at each state and subsequently using the advisors to guide action selection. Also, we provide theoretical convergence and sample complexity guarantees. Experimentally, we validate our approach in three different test-beds and show that our algorithms give better performances than baselines, can effectively integrate the combined expertise of different advisors, and learn to ignore bad advice.

4. Mean Field MARL

   Decentralized Mean Field Games

   Sriram Ganapathi Subramanian, Matthew Taylor, Mark Crowley, and Pascal Poupart.

   In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2022) Virtual, feb, 2022.

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   Multiagent reinforcement learning algorithms have not been widely adopted in large scale environments with many agents as they often scale poorly with the number of agents. Using mean field theory to aggregate agents has been proposed as a solution to this problem. However, almost all previous methods in this area make a strong assumption of a centralized system where all the agents in the environment learn the same policy and are effectively indistinguishable from each other. In this paper, we relax this assumption about indistinguishable agents and propose a new mean field system known as Decentralized Mean Field Games, where each agent can be quite different from others. All agents learn independent policies in a decentralized fashion, based on their local observations. We define a theoretical solution concept for this system and provide a fixed point guarantee for a Q-learning based algorithm in this system. A practical consequence of our approach is that we can address a ‘chicken-and-egg’ problem in empirical mean field reinforcement learning algorithms. Further, we provide Q-learning and actor-critic algorithms that use the decentralized mean field learning approach and give stronger performances compared to common baselines in this area. In our setting, agents do not need to be clones of each other and learn in a fully decentralized fashion. Hence, for the first time, we show the application of mean field learning methods in fully competitive environments, large-scale continuous action space environments, and other environments with heterogeneous agents. Importantly, we also apply the mean field method in a ride-sharing problem using a real-world dataset. We propose a decentralized solution to this problem, which is more practical than existing centralized training methods.

5. MARLEmpircal

   Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments

   Ken Ming Lee, Sriram Ganapathi Subramanian, and Mark Crowley

   Frontiers in Artificial Intelligence. 2022.

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   Independent reinforcement learning algorithms have no theoretical guarantees for finding the best policy in multi-agent settings. However, in practice, prior works have reported good performance with independent algorithms in some domains and bad performance in others. Moreover, a comprehensive study of the strengths and weaknesses of independent algorithms is lacking in the literature. In this paper, we carry out an empirical comparison of the performance of independent algorithms on seven PettingZoo environments that span the three main categories of multi-agent environments, i.e., cooperative, competitive, and mixed. For the cooperative setting, we show that independent algorithms can perform on par with multi-agent algorithms in fully-observable environments, while adding recurrence improves the learning of independent algorithms in partially-observable environments. In the competitive setting, independent algorithms can perform on par or better than multi-agent algorithms, even in more challenging environments. We also show that agents trained via independent algorithms learn to perform well individually, but fail to learn to cooperate with allies and compete with enemies in mixed environments.