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Reinforcement Learning
ServiceNow Research
Reinforcement Learning
Choreographer: Learning and Adapting Skills in Imagination
Unsupervised skill learning aims to learn a rich repertoire of behaviors without external supervision, providing artificial agents with …
Pietro Mazzaglia
,
Tim Verbelen
,
Bart Dhoedt
,
Alexandre Lacoste
,
Sai Rajeswar Mudumba
International Conference of Learning Representations (ICLR), 2023.
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Leveraging Human Preferences to Master Poetry
Large language models have been fine-tuned to learn poetry via supervised learning on a dataset containing relevant examples. However, …
Rafael Pardinas
,
Gabriel Huang
,
David Vazquez
,
Alexandre Piche
AAAI Workshops, 2023.
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Haptics-based Curiosity for Sparse-reward Tasks
Robots in many real-world settings have access to force/torque sensors in their gripper and tactile sensing is often necessary in tasks …
Sai Rajeswar
,
Cyril Ibrahim
,
Nitin Surya
,
Florian Golemo
,
David Vazquez
,
Aaron Courville
,
Pedro O. Pinheiro
Conference on Robot Learning (CoRL), 2022.
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Choreographer: Learning and Adapting Skills in Imagination
Unsupervised skill learning aims to learn a rich repertoire of behaviors without external supervision, providing artificial agents with …
Pietro Mazzaglia
,
Tim Verbelen
,
Bart Dhoedt
,
Alexandre Lacoste
,
Sai Rajeswar Mudumba
Workshop at the Neural Information Processing Systems (NeurIPS), 2022.
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Implicit Offline Reinforcement Learning via Supervised Learning
Offline Reinforcement Learning (RL) via Supervised Learning is a simple and effective way to learn robotic skills from a dataset of …
Alexandre Piche
,
Rafael Pardinas
,
David Vazquez
,
Igor Mordatch
,
Christopher Pal
Workshop at the Neural Information Processing Systems (NeurIPS), 2022.
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Using Confounded Data in Offline RL
In this work we consider the problem of confounding in offline RL, also referred to as the delusion problem. While it is known that …
Maxime Gasse
,
Damien Grasset
,
Guillaume Gaudron
,
Pierre-Yves Oudeyer
Workshop at the Neural Information Processing Systems (NeurIPS), 2022.
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Scaling up ML-based Black-box Planning with Partial STRIPS Models
A popular approach for sequential decision-making is to perform simulator-based search guided with Machine Learning (ML) methods like …
Matias Greco
,
Alvaro Torralba
,
Jorge Baier
,
Hector Palacios
Workshop at International Join Conference on Artificial Intelligence (IJCAI), 2022.
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Direct Behavior Specification via Constrained Reinforcement Learning
The standard formulation of Reinforcement Learning lacks a practical way of specifying what are admissible and forbidden behaviors. …
Julien Roy
,
Roger Girgis
,
Joshua Romoff
,
Pierre-Luc Bacon
,
Christopher Pal
International Conference on Machine Learning (ICML), 2022.
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Unsupervised Model-based Pre-training for Data-efficient Reinforcement Learning from Pixels
Reinforcement learning (RL) aims at autonomously performing complex tasks. To this end, a reward signal is used to steer the learning …
Sai Rajeswar Mudumba
,
Pietro Mazzaglia
,
Tim Verbelen
,
Alexandre Piche
,
Aaron Courville
,
Alexandre Lacoste
Workshop at the International Conference on Machine Learning (ICML), 2022.
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Scaling up ML-based Black-box Planning with Partial STRIPS Models
A popular approach for sequential decision-making is to perform simulator-based search guided with Machine Learning (ML) methods like …
Matias Greco
,
Alvaro Torralba
,
Jorge Baier
,
Hector Palacios
ICAPS'22 Workshop on Reliable Data-Driven Planning and Scheduling, 2022.
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