Shagun Sodhani

Learning Robust State Abstractions for Hidden-Parameter Block MDPs

2021-01-01

ICLR (publié)

openreview.net

A Simple and Effective Model for Multi-Hop Question Generation

Jimmy Lei Ba

Jamie Ryan Kiros

Geoffrey E Hin-602

Peter W. Battaglia

Jessica Blake

Chandler Hamrick

Vic-613 tor Bapst

Alvaro Sanchez

Vinicius Zambaldi

M. Malinowski

Andrea Tacchetti

David Raposo

Tom B. Brown

Benjamin Mann

Nick Ryder

Melanie Subbiah

Jared Kaplan

Prafulla Dhariwal

Arvind Neelakantan

Pranav Shyam … (voir 72 de plus)

Girish Sastry

William L. Hamilton

Clutrr

Nitish Srivastava

Geoffrey Hinton

Alex Krizhevsky

Ilya Sutskever

Ruslan Salakhutdinov. 2014

Gabriel Stanovsky

Julian Michael

Luke Zettlemoyer

Dan Su

Yan Xu

Wenliang Dai

Ziwei Ji

Tiezheng Yu

Minghao Tu

Kevin Huang

Guangtao Wang

Jing Huang

Ashish Vaswani

Noam M. Shazeer

Niki Parmar

Jakob Uszkoreit

Llion Jones

Aidan N. Gomez

Łukasz Kaiser

Illia Polosukhin. 2017

Attention

Petar Veliˇckovi´c

Guillem Cucurull

Arantxa Casanova

Adriana Romero Soriano

Pietro Lio’

Johannes Welbl

Pontus Stenetorp

Yonghui Wu

Mike Schuster

Quoc Zhifeng Chen

Mohammad Le

Wolfgang Norouzi

Macherey

M. Krikun

Yuan Cao

Qin Gao

William W. Cohen

Jianxing Yu

Xiaojun Quan

Qinliang Su

Jian Yin

Yuyu Zhang

Hanjun Dai

Zornitsa Kozareva

Cheng Zhao

Chenyan Xiong

Corby Rosset

Xia

Paul Song

Bennett Saurabh

Tiwary

Yao Zhao

Xiaochuan Ni

Yuanyuan Ding

Qingyu Zhou

Nan Yang

Furu Wei

Chuanqi Tan

Previous research on automated question gen-001 eration has almost exclusively focused on gen-002 erating factoid questions whose answers ca… (voir plus)n 003 be extracted from a single document. How-004 ever, there is an increasing interest in develop-005 ing systems that are capable of more complex 006 multi-hop question generation (QG), where an-007 swering the question requires reasoning over 008 multiple documents. In this work, we pro-009 pose a simple and effective approach based on 010 the transformer model for multi-hop QG. Our 011 approach consists of specialized input repre-012 sentations, a supporting sentence classiﬁcation 013 objective, and training data weighting. Prior 014 work on multi-hop QG considers the simpli-015 ﬁed setting of shorter documents and also ad-016 vocates the use of entity-based graph struc-017 tures as essential ingredients in model design. 018 On the contrary, we showcase that our model 019 can scale to the challenging setting of longer 020 documents as input, does not rely on graph 021 structures, and substantially outperforms the 022 state-of-the-art approaches as measured by au-023 tomated metrics and human evaluation. 024

2021-01-01

(publié)

www.semanticscholar.org

Invariant Causal Prediction for Block MDPs

Amy Zhang

Clare Lyle

Angelos Filos

Marta Z. Kwiatkowska

Joelle Pineau

Yarin Gal

Doina Precup

Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challenges. … (voir plus)In this paper, we consider the problem of learning abstractions that generalize in block MDPs, families of environments with a shared latent state space and dynamics structure over that latent space, but varying observations. We leverage tools from causal inference to propose a method of invariant prediction to learn model-irrelevance state abstractions (MISA) that generalize to novel observations in the multi-environment setting. We prove that for certain classes of environments, this approach outputs with high probability a state abstraction corresponding to the causal feature set with respect to the return. We further provide more general bounds on model error and generalization error in the multi-environment setting, in the process showing a connection between causal variable selection and the state abstraction framework for MDPs. We give empirical evidence that our methods work in both linear and nonlinear settings, attaining improved generalization over single- and multi-task baselines.

2020-11-21

Proceedings of the 37th International Conference on Machine Learning (publié)

proceedings.mlr.press

Multi-Task Reinforcement Learning as a Hidden-Parameter Block MDP

Multi-task reinforcement learning is a rich paradigm where information from previously seen environments can be leveraged for better perform… (voir plus)ance and improved sample-efficiency in new environments. In this work, we leverage ideas of common structure underlying a family of Markov decision processes (MDPs) to improve performance in the few-shot regime. We use assumptions of structure from Hidden-Parameter MDPs and Block MDPs to propose a new framework, HiP-BMDP, and approach for learning a common representation and universal dynamics model. To this end, we provide transfer and generalization bounds based on task and state similarity, along with sample complexity bounds that depend on the aggregate number of samples across tasks, rather than the number of tasks, a significant improvement over prior work. To demonstrate the efficacy of the proposed method, we empirically compare and show improvements against other multi-task and meta-reinforcement learning baselines.

2020-07-14

ArXiv (prépublication)

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Miles Brundage

Shahar Avin

Jasmine Wang

Haydn Belfield

Gretchen Krueger

Gillian K. Hadfield

Heidy Khlaaf

Jingying Yang

H. Toner

Ruth Catherine Fong

Tegan Maharaj

Pang Wei Koh

Sara Hooker

Jade Leung

Andrew John Trask

Emma Bluemke

Jonathan Lebensbold

Cullen C. O'keefe

Mark Koren

Th'eo Ryffel … (voir 39 de plus)

JB Rubinovitz

Tamay Besiroglu

Federica Carugati

Jack Clark

Peter Eckersley

Sarah de Haas

Maritza L. Johnson

Ben Laurie

Alex Ingerman

Igor Krawczuk

Amanda Askell

Rosario Cammarota

A. Lohn

David Scott Krueger

Charlotte Stix

Peter Mark Henderson

Logan Graham

Carina E. A. Prunkl

Bianca Martin

Elizabeth Seger

Noa Zilberman

Sean O hEigeartaigh

Frens Kroeger

Girish Sastry

R. Kagan

Adrian Weller

Brian Shek-kam Tse

Elizabeth Barnes

Allan Dafoe

Paul D. Scharre

Ariel Herbert-Voss

Martijn Rasser

Carrick Flynn

Thomas Krendl Gilbert

Lisa Dyer

Saif M. Khan

Markus Anderljung

2020-04-15

ArXiv (prépublication)

Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives

Anirudh Goyal

Jonathan Binas

Xue Bin Peng

Sergey Levine

Reinforcement learning agents that operate in diverse and complex environments can benefit from the structured decomposition of their behavi… (voir plus)or. Often, this is addressed in the context of hierarchical reinforcement learning, where the aim is to decompose a policy into lower-level primitives or options, and a higher-level meta-policy that triggers the appropriate behaviors for a given situation. However, the meta-policy must still produce appropriate decisions in all states. In this work, we propose a policy design that decomposes into primitives, similarly to hierarchical reinforcement learning, but without a high-level meta-policy. Instead, each primitive can decide for themselves whether they wish to act in the current state. We use an information-theoretic mechanism for enabling this decentralized decision: each primitive chooses how much information it needs about the current state to make a decision and the primitive that requests the most information about the current state acts in the world. The primitives are regularized to use as little information as possible, which leads to natural competition and specialization. We experimentally demonstrate that this policy architecture improves over both flat and hierarchical policies in terms of generalization.

2020-01-01

ICLR.cc/2020/Conference (poster)

openreview.net

Toward Training Recurrent Neural Networks for Lifelong Learning

Sarath Chandar

2020-01-01

Neural Computation (publié)

doi.org

Learning Powerful Policies by Using Consistent Dynamics Model

Sergey Levine

Model-based Reinforcement Learning approaches have the promise of being sample efficient. Much of the progress in learning dynamics models i… (voir plus)n RL has been made by learning models via supervised learning. But traditional model-based approaches lead to `compounding errors' when the model is unrolled step by step. Essentially, the state transitions that the learner predicts (by unrolling the model for multiple steps) and the state transitions that the learner experiences (by acting in the environment) may not be consistent. There is enough evidence that humans build a model of the environment, not only by observing the environment but also by interacting with the environment. Interaction with the environment allows humans to carry out experiments: taking actions that help uncover true causal relationships which can be used for building better dynamics models. Analogously, we would expect such interactions to be helpful for a learning agent while learning to model the environment dynamics. In this paper, we build upon this intuition by using an auxiliary cost function to ensure consistency between what the agent observes (by acting in the real world) and what it imagines (by acting in the `learned' world). We consider several tasks - Mujoco based control tasks and Atari games - and show that the proposed approach helps to train powerful policies and better dynamics models.

2019-06-11

ArXiv (prépublication)

Environments for Lifelong Reinforcement Learning

To achieve general artificial intelligence, reinforcement learning (RL) agents should learn not only to optimize returns for one specific ta… (voir plus)sk but also to constantly build more complex skills and scaffold their knowledge about the world, without forgetting what has already been learned. In this paper, we discuss the desired characteristics of environments that can support the training and evaluation of lifelong reinforcement learning agents, review existing environments from this perspective, and propose recommendations for devising suitable environments in the future.

2018-11-26

ArXiv (prépublication)

On Training Recurrent Neural Networks for Lifelong Learning

Sarath Chandar

Catastrophic forgetting and capacity saturation are the central challenges of any parametric lifelong learning system. In this work, we stud… (voir plus)y these challenges in the context of sequential supervised learning with emphasis on recurrent neural networks. To evaluate the models in the lifelong learning setting, we propose a curriculum-based, simple, and intuitive benchmark where the models are trained on tasks with increasing levels of difficulty. To measure the impact of catastrophic forgetting, the model is tested on all the previous tasks as it completes any task. As a step towards developing true lifelong learning systems, we unify Gradient Episodic Memory (a catastrophic forgetting alleviation approach) and Net2Net(a capacity expansion approach). Both these models are proposed in the context of feedforward networks and we evaluate the feasibility of using them for recurrent networks. Evaluation on the proposed benchmark shows that the unified model is more suitable than the constituent models for lifelong learning setting.

2018-11-16

ArXiv (prépublication)