Publications

Dealing with Non-Stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning
Hadi Nekoei
Akilesh Badrinaaraayanan
Amit Sinha
Mohammad Amini
Janarthanan Rajendran
Aditya Mahajan
Sarath Chandar
Decentralized cooperative multi-agent deep reinforcement learning (MARL) can be a versatile learning framework, particularly in scenarios wh… (voir plus)ere centralized training is either not possible or not practical. One of the critical challenges in decentralized deep MARL is the non-stationarity of the learning environment when multiple agents are learning concurrently. A commonly used and efficient scheme for decentralized MARL is independent learning in which agents concurrently update their policies independently of each other. We first show that independent learning does not always converge, while sequential learning where agents update their policies one after another in a sequence is guaranteed to converge to an agent-by-agent optimal solution. In sequential learning, when one agent updates its policy, all other agent's policies are kept fixed, alleviating the challenge of non-stationarity due to simultaneous updates in other agents' policies. However, it can be slow because only one agent is learning at any time. Therefore it might also not always be practical. In this work, we propose a decentralized cooperative MARL algorithm based on multi-timescale learning. In multi-timescale learning, all agents learn simultaneously, but at different learning rates. In our proposed method, when one agent updates its policy, other agents are allowed to update their policies as well, but at a slower rate. This speeds up sequential learning, while also minimizing non-stationarity caused by other agents updating concurrently. Multi-timescale learning outperforms state-of-the-art decentralized learning methods on a set of challenging multi-agent cooperative tasks in the epymarl(Papoudakis et al., 2020) benchmark. This can be seen as a first step towards more general decentralized cooperative deep MARL methods based on multi-timescale learning.
2023-11-19
Proceedings of The 2nd Conference on Lifelong Learning Agents (publié)
doi.org
proceedings.mlr.press
Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning
Safa Alver
Doina Precup
Learning models of the environment from pure interaction is often considered an essential component of building lifelong reinforcement learn… (voir plus)ing agents. However, the common practice in model-based reinforcement learning is to learn models that model every aspect of the agent’s environment, regardless of whether they are important in coming up with optimal decisions or not. In this paper, we argue that such models are not particularly well-suited for performing scalable and robust planning in lifelong reinforcement learning scenarios and we propose new kinds of models that only model the relevant aspects of the environment, which we call \emph{minimal value-equivalent partial models}. After providing a formal definition for these models, we provide theoretical results demonstrating the scalability advantages of performing planning with such models and then perform experiments to empirically illustrate our theoretical results. Then, we provide some useful heuristics on how to learn these kinds of models with deep learning architectures and empirically demonstrate that models learned in such a way can allow for performing planning that is robust to distribution shifts and compounding model errors. Overall, both our theoretical and empirical results suggest that minimal value-equivalent partial models can provide significant benefits to performing scalable and robust planning in lifelong reinforcement learning scenarios.
2023-11-19
Proceedings of The 2nd Conference on Lifelong Learning Agents (publié)
doi.org
proceedings.mlr.press
Responsible AI Research Needs Impact Statements Too
A.R. Olteanu
Michael Ekstrand
Carlos Castillo
Jina Suh
All types of research, development, and policy work can have unintended, adverse consequences - work in responsible artificial intelligence … (voir plus)(RAI), ethical AI, or ethics in AI is no exception.
2023-11-19
ArXiv (prépublication)
doi.org
arxiv.org
Substituting Data Annotation with Balanced Neighbourhoods and Collective Loss in Multi-label Text Classification
Muberra Ozmen
Joseph Cotnareanu
Mark J. Coates
Multi-label text classification (MLTC) is the task of assigning multiple labels to a given text, and has a wide range of application domains… (voir plus). Most existing approaches require an enormous amount of annotated data to learn a classifier and/or a set of well-defined constraints on the label space structure, such as hierarchical relations which may be complicated to provide as the number of labels increases. In this paper, we study the MLTC problem in annotation-free and scarce-annotation settings in which the magnitude of available supervision signals is linear to the number of labels. Our method follows three steps, (1) mapping input text into a set of preliminary label likelihoods by natural language inference using a pre-trained language model, (2) calculating a signed label dependency graph by label descriptions, and (3) updating the preliminary label likelihoods with message passing along the label dependency graph, driven with a collective loss function that injects the information of expected label frequency and average multi-label cardinality of predictions. The experiments show that the proposed framework achieves effective performance under low supervision settings with almost imperceptible computational and memory overheads added to the usage of pre-trained language model outperforming its initial performance by 70% in terms of example-based F1 score.
2023-11-19
Proceedings of The 2nd Conference on Lifelong Learning Agents (publié)
proceedings.mlr.press
proceedings.mlr.press
Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges
Massimo Caccia
Jonas Mueller
Taesup Kim
Laurent Charlin
Rasool Fakoor
2023-11-19
Proceedings of The 2nd Conference on Lifelong Learning Agents (publié)
proceedings.mlr.press
proceedings.mlr.press
Towards Few-Shot Coordination: Revisiting Ad-Hoc Teamplay Challenge in the Game of Hanabi
Hadi Nekoei
Xutong Zhao
Janarthanan Rajendran
Miao Liu
Sarath Chandar
Cooperative Multi-agent Reinforcement Learning (MARL) algorithms with Zero-Shot Coordination (ZSC) have gained significant attention in rece… (voir plus)nt 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.
2023-11-19
Proceedings of The 2nd Conference on Lifelong Learning Agents (publié)
doi.org
proceedings.mlr.press
Assessing the Security of GitHub Copilot's Generated Code - A Targeted Replication Study
Vahid Majdinasab
Michael Joshua Bishop
Shawn Rasheed
Arghavan Moradi Dakhel
Amjed Tahir
Foutse Khomh
2023-11-17
ArXiv (prépublication)
doi.org
arxiv.org
MUDiff: Unified Diffusion for Complete Molecule Generation
Chenqing Hua
Sitao Luan
Minkai Xu
Zhitao Ying
Rex Ying
Jie Fu
Stefano Ermon
Doina Precup
2023-11-17
logconference.io/LOG/2023/Conference (poster)
doi.org
proceedings.mlr.press
The evidence mismatch in pediatric surgical practice
Marina Broomfield
Zena Agabani
Elena Guadagno
Dan Poenaru
Robert Baird
2023-11-17
Pediatric surgery international (Print) (publié)
doi.org
Aperiodic activity as a central neural feature of hypnotic susceptibility outside of hypnosis
Mathieu Landry
Jason da Silva Castanheira
Catherine Boisvert
Floriane Rousseaux
Jérôme Sackur
Amir Raz
Philippe Richebé
David Ogez
Pierre Rainville
Karim Jerbi
Hypnotic phenomena reflect the ability to alter one’s subjective experiences based on targeted verbal suggestions. This ability varies gre… (voir plus)atly in the population. The brain correlates to explain this variability remain elusive. Addressing this gap, our study employs machine learning to predict hypnotic susceptibility. By recording electroencephalography (EEG) before and after a hypnotic induction and analyzing diverse neurophysiological features, we were able to determine that several features differentiate between high and low hypnotic susceptible individuals both at baseline and during hypnosis. Our analysis revealed that the paramount discriminative feature is non-oscillatory EEG activity before the induction—a new finding in the field. This outcome aligns with the idea that hypnotic susceptibility represents a latent trait observable through a plain five-minutes resting-state EEG.
2023-11-16
bioRxiv (prépublication)
doi.org
Differentiable visual computing for inverse problems and machine learning
Andrew Spielberg
Fangcheng Zhong
Konstantinos Rematas
Krishna Murthy
Cengiz Oztireli
Tzu-Mao Li
D. Nowrouzezahrai
2023-11-16
Nature Machine Intelligence (publié)
doi.org
arxiv.org
Adaptive Integration of Categorical and Multi-relational Ontologies with EHR Data for Medical Concept Embedding
Chin Wang Cheong
Kejing Yin
William K. Cheung
Benjamin C. M. Fung
Jonathan Poon
2023-11-13
ACM Transactions on Intelligent Systems and Technology (publié)
doi.org