no code implementations • ICML 2020 • Hengyuan Hu, Alexander Peysakhovich, Adam Lerer, Jakob Foerster
We consider the problem of zero-shot coordination - constructing AI agents that can coordinate with novel partners they have not seen before (e. g. humans).
Multi-agent Reinforcement Learning Reinforcement Learning (RL)
no code implementations • 28 Sep 2023 • Alexander Peysakhovich, Adam Lerer
Current language models often fail to incorporate long contexts efficiently during generation.
1 code implementation • Science 2022 • Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, Andrew Goff, Jonathan Gray, Hengyan Hu, Athul Paul Jacob, Mojtaba Komeili, Karthik Konath, Minae Kwon, Adam Lerer, Mike Lewis, Alexander H. Miller, Sash Mitts, Aditya Renduchintala, Stephen Roller, Dirk Rowe, Weiyan Shi, Joe Spisak, Alexander Wei, David Wu, Hugh Zhang, Markus Zijlstra
Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge.
no code implementations • 11 Oct 2022 • Hengyuan Hu, David J Wu, Adam Lerer, Jakob Foerster, Noam Brown
First, we show that our method outperforms experts when playing with a group of diverse human players in ad-hoc teams.
1 code implementation • 11 Oct 2022 • Anton Bakhtin, David J Wu, Adam Lerer, Jonathan Gray, Athul Paul Jacob, Gabriele Farina, Alexander H Miller, Noam Brown
We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of human play while simultaneously training an agent that responds well to this human model.
no code implementations • 10 Jun 2022 • Leon Yao, Caroline Lo, Israel Nir, Sarah Tan, Ariel Evnine, Adam Lerer, Alex Peysakhovich
Learning heterogeneous treatment effects (HTEs) is an important problem across many fields.
no code implementations • 14 Dec 2021 • Athul Paul Jacob, David J. Wu, Gabriele Farina, Adam Lerer, Hengyuan Hu, Anton Bakhtin, Jacob Andreas, Noam Brown
We consider the task of building strong but human-like policies in multi-agent decision-making problems, given examples of human behavior.
1 code implementation • NeurIPS 2021 • Anton Bakhtin, David Wu, Adam Lerer, Noam Brown
Additionally, we extend our methods to full-scale no-press Diplomacy and for the first time train an agent from scratch with no human data.
no code implementations • 3 Jul 2021 • Ellen D. Zhong, Adam Lerer, Joseph H. Davis, Bonnie Berger
Although reconstruction algorithms typically model the 3D volume as a generic function parameterized as a voxel array or neural network, the underlying atomic structure of the protein of interest places well-defined physical constraints on the reconstructed structure.
no code implementations • 16 Jun 2021 • Hengyuan Hu, Adam Lerer, Noam Brown, Jakob Foerster
Search is an important tool for computing effective policies in single- and multi-agent environments, and has been crucial for achieving superhuman performance in several benchmark fully and partially observable games.
5 code implementations • 6 Mar 2021 • Hengyuan Hu, Adam Lerer, Brandon Cui, David Wu, Luis Pineda, Noam Brown, Jakob Foerster
Policies learned through self-play may adopt arbitrary conventions and implicitly rely on multi-step reasoning based on fragile assumptions about other agents' actions and thus fail when paired with humans or independently trained agents at test time.
no code implementations • ICCV 2021 • Ellen D. Zhong, Adam Lerer, Joseph H. Davis, Bonnie Berger
In this work we describe cryoDRGN2, an ab initio reconstruction algorithm, which can jointly estimate image poses and learn a neural model of a distribution of 3D structures on real heterogeneous cryo-EM data.
1 code implementation • 19 Nov 2020 • Lingfan Yu, Jiajun Shen, Jinyang Li, Adam Lerer
Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data.
Ranked #20 on Node Property Prediction on ogbn-mag
no code implementations • NeurIPS 2020 • Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alex Peysakhovich, Aldo Pacchiano, Jakob Foerster
In the era of ever decreasing loss functions, SGD and its various offspring have become the go-to optimization tool in machine learning and are a key component of the success of deep neural networks (DNNs).
no code implementations • ICLR 2021 • Jonathan Gray, Adam Lerer, Anton Bakhtin, Noam Brown
Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings.
1 code implementation • NeurIPS 2020 • Noam Brown, Anton Bakhtin, Adam Lerer, Qucheng Gong
This paper presents ReBeL, a general framework for self-play reinforcement learning and search that provably converges to a Nash equilibrium in any two-player zero-sum game.
1 code implementation • 18 Jun 2020 • Eric Steinberger, Adam Lerer, Noam Brown
We introduce DREAM, a deep reinforcement learning algorithm that finds optimal strategies in imperfect-information games with multiple agents.
2 code implementations • 6 Mar 2020 • Hengyuan Hu, Adam Lerer, Alex Peysakhovich, Jakob Foerster
We consider the problem of zero-shot coordination - constructing AI agents that can coordinate with novel partners they have not seen before (e. g. humans).
10 code implementations • 5 Dec 2019 • Adam Lerer, Hengyuan Hu, Jakob Foerster, Noam Brown
The first one, single-agent search, effectively converts the problem into a single agent setting by making all but one of the agents play according to the agreed-upon policy.
2 code implementations • NeurIPS 2019 • Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala
Deep learning frameworks have often focused on either usability or speed, but not both.
no code implementations • NeurIPS 2019 • Alexander Peysakhovich, Christian Kroer, Adam Lerer
We consider the problem of using logged data to make predictions about what would happen if we changed the `rules of the game' in a multi-agent system.
1 code implementation • 28 Mar 2019 • Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, Alex Peysakhovich
Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks.
Ranked #1 on Link Prediction on YouTube (Macro F1 metric)
4 code implementations • 1 Nov 2018 • Noam Brown, Adam Lerer, Sam Gross, Tuomas Sandholm
This paper introduces Deep Counterfactual Regret Minimization, a form of CFR that obviates the need for abstraction by instead using deep neural networks to approximate the behavior of CFR in the full game.
no code implementations • 26 Jun 2018 • Adam Lerer, Alexander Peysakhovich
When there are multiple possible conventions we show that learning a policy via multi-agent reinforcement learning (MARL) is likely to find policies which achieve high payoffs at training time but fail to coordinate with the real group into which the agent enters.
1 code implementation • 20 Mar 2018 • Ronan Riochet, Mario Ynocente Castro, Mathieu Bernard, Adam Lerer, Rob Fergus, Véronique Izard, Emmanuel Dupoux
In order to reach human performance on complexvisual tasks, artificial systems need to incorporate a sig-nificant amount of understanding of the world in termsof macroscopic objects, movements, forces, etc.
no code implementations • ICML 2018 • Amy Zhang, Adam Lerer, Sainbayar Sukhbaatar, Rob Fergus, Arthur Szlam
The tasks that an agent will need to solve often are not known during training.
1 code implementation • NIPS 2017 2017 • Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, Adam Lerer
In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on machine learning models.
no code implementations • ICLR 2018 • Alexander Peysakhovich, Adam Lerer
We show how to construct such strategies using deep reinforcement learning techniques and demonstrate, both analytically and experimentally, that they are effective in social dilemmas beyond simple matrix games.
no code implementations • 8 Sep 2017 • Alexander Peysakhovich, Adam Lerer
We extend existing work on reward-shaping in multi-agent reinforcement learning and show that that making a single agent prosocial, that is, making them care about the rewards of their partners can increase the probability that groups converge to good outcomes.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • ICLR 2018 • Adam Lerer, Alexander Peysakhovich
Social dilemmas are situations where individuals face a temptation to increase their payoffs at a cost to total welfare.
1 code implementation • 7 Apr 2016 • Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O. Pinheiro, Sam Gross, Soumith Chintala, Piotr Dollár
To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that give the detector access to features at multiple network layers, (2) a foveal structure to exploit object context at multiple object resolutions, and (3) an integral loss function and corresponding network adjustment that improve localization.
Ranked #104 on Instance Segmentation on COCO test-dev
3 code implementations • 3 Mar 2016 • Adam Lerer, Sam Gross, Rob Fergus
Wooden blocks are a common toy for infants, allowing them to develop motor skills and gain intuition about the physical behavior of the world.