ServiceNow AI Research

Reinforcement Learning

Using Confounded Data in Latent Model-Based Reinforcement Learning
Using Confounded Data in Latent Model-Based Reinforcement Learning
In the presence of confounding, naively using off-the-shelf offline reinforcement learning (RL) algorithms leads to sub-optimal …
Maxime Gasse, Damien Grasset, Pierre-Yves Oudeyer, Guillaume Gaudron
Transactions on Machine Learning Research (TMLR),  2023.
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Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
Controlling artificial agents from visual sensory data is an arduous task. Reinforcement learning (RL) algorithms can succeed but …
Sai Rajeswar Mudumba, Pietro Mazzaglia, Tim Verbelen, Alexandre Piche, Bart Dhoedt, Aaron Courville, Alexandre Lacoste
International Conference on Machine Learning (ICML),  2023.
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Choreographer: Learning and Adapting Skills in Imagination
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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Deep Hyperbolic Reinforcement Learning for Continuous Control
Deep Hyperbolic Reinforcement Learning for Continuous Control
Integrating hyperbolic representations with Deep Reinforcement Learning (DRL) has recently been proposed as a promising approach for …
Omar Salemohamed, Edoardo Cetin, Sai Rajeswar Mudumba, Arnab Mondal
ICLR, Tiny Papers,  2023.
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Leveraging Human Preferences to Master Poetry
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
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 Mudumba, 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
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
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
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
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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