Pierre-Luc Bacon | DeepAI

DeepAI AI Chat

Featured Co-authors

Yoshua Bengio
438 publications
Shie Mannor
153 publications
Aaron Courville
151 publications
Doina Precup
139 publications
Jian Tang
125 publications
Joelle Pineau
116 publications
Martha White
64 publications
Emma Brunskill
63 publications
Christopher Pal
60 publications
Pascal Vincent
56 publications
Petar Veličković
52 publications

∙ 09/13/2022

Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization

The ability to accelerate the design of biological sequences can have a ...

21 Leo Feng, et al. ∙

∙ 05/16/2022

The Primacy Bias in Deep Reinforcement Learning

This work identifies a common flaw of deep reinforcement learning (RL) a...

0 Evgenii Nikishin, et al. ∙

∙ 03/02/2022

Continuous-Time Meta-Learning with Forward Mode Differentiation

Drawing inspiration from gradient-based meta-learning methods with infin...

0 Tristan Deleu, et al. ∙

∙ 02/22/2022

Myriad: a real-world testbed to bridge trajectory optimization and deep learning

We present Myriad, a testbed written in JAX for learning and planning in...

0 Nikolaus H. R. Howe, et al. ∙

∙ 12/22/2021

Direct Behavior Specification via Constrained Reinforcement Learning

The standard formulation of Reinforcement Learning lacks a practical way...

0 Julien Roy, et al. ∙

∙ 10/11/2021

Neural Algorithmic Reasoners are Implicit Planners

Implicit planning has emerged as an elegant technique for combining lear...

17 Andreea Deac, et al. ∙

∙ 06/06/2021

Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation

The shortcomings of maximum likelihood estimation in the context of mode...

0 Evgenii Nikishin, et al. ∙

∙ 03/10/2021

An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning

How do we formalize the challenge of credit assignment in reinforcement ...

0 Dilip Arumugam, et al. ∙

∙ 10/25/2020

XLVIN: eXecuted Latent Value Iteration Nets

Value Iteration Networks (VINs) have emerged as a popular method to inco...

2 Andreea Deac, et al. ∙

∙ 09/26/2020

Graph neural induction of value iteration

Many reinforcement learning tasks can benefit from explicit planning bas...

2 Andreea Deac, et al. ∙

∙ 07/06/2020

TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?

We investigate whether Jacobi preconditioning, accounting for the bootst...

0 Joshua Romoff, et al. ∙

∙ 02/26/2020

Policy Evaluation Networks

Many reinforcement learning algorithms use value functions to guide the ...

6 Jean Harb, et al. ∙

∙ 01/01/2020

Options of Interest: Temporal Abstraction with Interest Functions

Temporal abstraction refers to the ability of an agent to use behaviours...

14 Khimya Khetarpal, et al. ∙

∙ 12/11/2019

Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods

The policy gradient theorem is defined based on an objective with respec...

0 Riashat Islam, et al. ∙

∙ 10/21/2019

All-Action Policy Gradient Methods: A Numerical Integration Approach

While often stated as an instance of the likelihood ratio trick [Rubinst...

0 Benjamin Petit, et al. ∙

∙ 10/15/2019

Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling

We establish a connection between the importance sampling estimators typ...

0 Yao Liu, et al. ∙

∙ 11/16/2018

The Barbados 2018 List of Open Issues in Continual Learning

We want to make progress toward artificial general intelligence, namely ...

0 Tom Schaul, et al. ∙

∙ 02/09/2018

Learning Robust Options

Robust reinforcement learning aims to produce policies that have strong ...

0 Daniel J. Mankowitz, et al. ∙

∙ 11/30/2017

Learnings Options End-to-End for Continuous Action Tasks

We present new results on learning temporally extended actions for conti...

0 Martin Klissarov, et al. ∙

∙ 11/10/2017

Learning with Options that Terminate Off-Policy

A temporally abstract action, or an option, is specified by a policy and...

0 Anna Harutyunyan, et al. ∙

∙ 09/14/2017

When Waiting is not an Option : Learning Options with a Deliberation Cost

Recent work has shown that temporally extended actions (options) can be ...

0 Jean Harb, et al. ∙

∙ 05/25/2017

Convergent Tree-Backup and Retrace with Function Approximation

Off-policy learning is key to scaling up reinforcement learning as it al...

0 Ahmed Touati, et al. ∙

∙ 12/03/2016

A Matrix Splitting Perspective on Planning with Options

We show that the Bellman operator underlying the options framework leads...

0 Pierre-Luc Bacon, et al. ∙

∙ 09/16/2016

The Option-Critic Architecture

Temporal abstraction is key to scaling up learning and planning in reinf...

0 Pierre-Luc Bacon, et al. ∙