Doina Precup

Sumana Basu

Collaborateur·rice alumni - McGill

Co-superviseur⋅e :

Adriana Romero Soriano

Collaborateur·rice alumni - McGill

Lynn Cherif

Collaborateur·rice alumni - McGill

Co-superviseur⋅e :

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Superviseur⋅e principal⋅e :

David Meger

Jonathan Colaço Carr

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Prakash Panangaden

Élodie Coté-Gauthier

Collaborateur·rice de recherche - McGill

Co-superviseur⋅e :

Isabeau Prémont-Schwarz

Franco Del Balso

Collaborateur·rice de recherche - UdeM

Jesse Farebrother

Doctorat - McGill

Superviseur⋅e principal⋅e :

Marc Gendron-Bellemare

Doctorat - McGill

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - Birla Institute of Technology

Howard Huang

Doctorat - McGill

Haque Ishfaq

Collaborateur·rice alumni - McGill

Mohammad Sami Nur Islam Islam

Maîtrise recherche - McGill

Arushi Jain

Collaborateur·rice alumni - McGill

Doctorat - Polytechnique

Doctorat - McGill

Postdoctorat - McGill

Jonathan Lebensold

Collaborateur·rice alumni - McGill

Collaborateur·rice alumni - McGill

Ray Luo

Doctorat - McGill

Superviseur⋅e principal⋅e :

G McCracken

Doctorat - McGill

Nazanin Mohammadi Sepahvand

Collaborateur·rice alumni - McGill

Shahrad Mohammadzadeh

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Gabriela Moisescu-Pareja

Collaborateur·rice de recherche - McGill

Co-superviseur⋅e :

Irina Rish

Padideh Nouri

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - McGill

Co-superviseur⋅e :

Nate Rahn

Doctorat - McGill

Superviseur⋅e principal⋅e :

Marc Gendron-Bellemare

Manoosh Samiei

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Nishanth Anand Vemgal

Doctorat - McGill

Doctorat - McGill

Co-superviseur⋅e :

Samira Ebrahimi Kahou

Zihan Wang

Doctorat - McGill

Skipper : combiner l’abstraction spatiale et temporelle afin d’améliorer la généralisation

Guangyuan Wang

Stagiaire de recherche - McGill

Steve Wen

Maîtrise recherche - McGill

Co-superviseur⋅e :

Gregory Dudek

Zijing Wu

Doctorat - McGill

Superviseur⋅e principal⋅e :

Doctorat - McGill

Harry Zhao

Collaborateur·rice alumni - McGill

Co-superviseur⋅e :

Billets de blogue

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22 février 2024

par

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

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Publications

Policy Evaluation Networks

Jean Harb

Tom Schaul

Pierre-Luc Bacon

Many reinforcement learning algorithms use value functions to guide the search for better policies. These methods estimate the value of a si… (voir plus)ngle policy while generalizing across many states. The core idea of this paper is to flip this convention and estimate the value of many policies, for a single set of states. This approach opens up the possibility of performing direct gradient ascent in policy space without seeing any new data. The main challenge for this approach is finding a way to represent complex policies that facilitates learning and generalization. To address this problem, we introduce a scalable, differentiable fingerprinting mechanism that retains essential policy information in a concise embedding. Our empirical results demonstrate that combining these three elements (learned Policy Evaluation Network, policy fingerprints, gradient ascent) can produce policies that outperform those that generated the training data, in zero-shot manner.

2020-02-25

ArXiv (prépublication)

oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions

David Venuto

Jhelum Chakravorty

Léonard Boussioux

Junhao Wang

Gavin McCracken

Explicit engineering of reward functions for given environments has been a major hindrance to reinforcement learning methods. While Inverse … (voir plus)Reinforcement Learning (IRL) is a solution to recover reward functions from demonstrations only, these learned rewards are generally heavily \textit{entangled} with the dynamics of the environment and therefore not portable or \emph{robust} to changing environments. Modern adversarial methods have yielded some success in reducing reward entanglement in the IRL setting. In this work, we leverage one such method, Adversarial Inverse Reinforcement Learning (AIRL), to propose an algorithm that learns hierarchical disentangled rewards with a policy over options. We show that this method has the ability to learn \emph{generalizable} policies and reward functions in complex transfer learning tasks, while yielding results in continuous control benchmarks that are comparable to those of the state-of-the-art methods.

2020-02-19

ArXiv (prépublication)

Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces

Thang Doan

We propose a general framework for policy representation for reinforcement learning tasks. This framework involves finding a low-dimensional… (voir plus) embedding of the policy on a reproducing kernel Hilbert space (RKHS). The usage of RKHS based methods allows us to derive strong theoretical guarantees on the expected return of the reconstructed policy. Such guarantees are typically lacking in black-box models, but are very desirable in tasks requiring stability. We conduct several experiments on classic RL domains. The results confirm that the policies can be robustly embedded in a low-dimensional space while the embedded policy incurs almost no decrease in return.

2020-02-06

(publié)

A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms

Philip Amortila

Prakash Panangaden

Bellemare Marc-Emmanuel

We present a distributional approach to theoretical analyses of reinforcement learning algorithms for constant step-sizes. We demonstrate it… (voir plus)s effectiveness by presenting simple and unified proofs of convergence for a variety of commonly-used methods. We show that value-based methods such as TD(

2019-12-31

AISTATS (publié)

proceedings.mlr.press

On Efficiency in Hierarchical Reinforcement Learning

Zheng Wen

Morteza Ibrahimi

Andre Barreto

Benjamin Van Roy

Satinder Singh

Hierarchical Reinforcement Learning (HRL) approaches promise to provide more efﬁcient solutions to sequential decision making problems, bo… (voir plus)th in terms of statistical as well as computational efﬁciency. While this has been demonstrated empirically over time in a variety of tasks, theoretical results quantifying the ben-eﬁts of such methods are still few and far between. In this paper, we discuss the kind of structure in a Markov decision process which gives rise to efﬁcient HRL methods. Speciﬁcally, we formalize the intuition that HRL can exploit well repeating "subMDPs", with similar reward and transition structure. We show that, under reasonable assumptions, a model-based Thompson sampling-style HRL algorithm that exploits this structure is statistically efﬁcient, as established through a ﬁnite-time regret bound. We also establish conditions under which planning with structure-induced options is near-optimal and computationally efﬁcient.

2019-12-31

Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (publié)

dblp.uni-trier.de

Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning

2019-12-31

International Conference on Artificial Intelligence and Statistics (publié)

proceedings.mlr.press

An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay

Scott Fujimoto

David Meger

Prioritized Experience Replay (PER) is a deep reinforcement learning technique in which agents learn from transitions sampled with non-unifo… (voir plus)rm probability proportionate to their temporal-difference error. We show that any loss function evaluated with non-uniformly sampled data can be transformed into another uniformly sampled loss function with the same expected gradient. Surprisingly, we find in some environments PER can be replaced entirely by this new loss function without impact to empirical performance. Furthermore, this relationship suggests a new branch of improvements to PER by correcting its uniformly sampled loss function equivalent. We demonstrate the effectiveness of our proposed modifications to PER and the equivalent loss function in several MuJoCo and Atari environments.

2019-12-31

Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (publié)

Forethought and Hindsight in Credit Assignment

Veronica Chelu

Hado van Hasselt

We address the problem of credit assignment in reinforcement learning and explore fundamental questions regarding the way in which an agent … (voir plus)can best use additional computation to propagate new information, by planning with internal models of the world to improve its predictions. Particularly, we work to understand the gains and peculiarities of planning employed as forethought via forward models or as hindsight operating with backward models. We establish the relative merits, limitations and complementary properties of both planning mechanisms in carefully constructed scenarios. Further, we investigate the best use of models in planning, primarily focusing on the selection of states in which predictions should be (re)-evaluated. Lastly, we discuss the issue of model estimation and highlight a spectrum of methods that stretch from explicit environment-dynamics predictors to more abstract planner-aware models.

2019-12-31

Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (publié)

Reward Propagation Using Graph Convolutional Networks

Martin Klissarov

Potential-based reward shaping provides an approach for designing good reward functions, with the purpose of speeding up learning. However, … (voir plus)automatically finding potential functions for complex environments is a difficult problem (in fact, of the same difficulty as learning a value function from scratch). We propose a new framework for learning potential functions by leveraging ideas from graph representation learning. Our approach relies on Graph Convolutional Networks which we use as a key ingredient in combination with the probabilistic inference view of reinforcement learning. More precisely, we leverage Graph Convolutional Networks to perform message passing from rewarding states. The propagated messages can then be used as potential functions for reward shaping to accelerate learning. We verify empirically that our approach can achieve considerable improvements in both small and high-dimensional control problems.

2019-12-31

NeurIPS (publié)

Reward Redistribution Mechanisms in Multi-agent Reinforcement Learning

Aly Ibrahim

Anirudha Jitani

Piracha

Daoud

In typical Multi-Agent Reinforcement Learning (MARL) settings, each agent acts to maximize its individual reward objective. However, for col… (voir plus)lective social welfare maximization, some agents may need to act non-selfishly. We propose a reward shaping mechanism using extrinsic motivation for achieving modularity and increased cooperation among agents in Sequential Social Dilemma (SSD) problems. Our mechanism, inspired by capitalism, provides extrinsic motivation to agents by redistributing a portion of collected re-wards based on each agent’s individual contribution towards team rewards. We demonstrate empirically that this mechanism leads to higher collective welfare relative to existing baselines. Furthermore, this reduces free rider issues and leads to more diverse policies. We evaluate our proposed mechanism for already specialised agents that are pre-trained for specific roles. We show that our mechanism, in the most challenging CleanUp environment, significantly out-performs two baselines (based roughly on socialism and anarchy) and accumulates 2-3 times higher rewards in an easier setting of the environment.

2019-12-31

(publié)

www.semanticscholar.org

Value Preserving State-Action Abstractions

David Abel

Nathan Umbanhowar

Khimya Khetarpal

Dilip Arumugam

Michael L. Littman

2019-12-31

International Conference on Artificial Intelligence and Statistics (publié)

proceedings.mlr.press

Value-driven Hindsight Modelling

Arthur Guez

Fabio Viola

Theophane Weber

Lars Buesing

Steven Kapturowski