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2020 – today
- 2024
[c9]Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff:
Motif: Intrinsic Motivation from Artificial Intelligence Feedback. ICLR 2024- [c8]Zhixuan Lin, Pierluca D'Oro, Evgenii Nikishin, Aaron C. Courville:
The Curse of Diversity in Ensemble-Based Exploration. ICLR 2024 - [c7]Michel Ma, Tianwei Ni, Clement Gehring, Pierluca D'Oro, Pierre-Luc Bacon:
Do Transformer World Models Give Better Policy Gradients? ICML 2024 - [i11]Michel Ma, Tianwei Ni, Clement Gehring, Pierluca D'Oro, Pierre-Luc Bacon:
Do Transformer World Models Give Better Policy Gradients? CoRR abs/2402.05290 (2024) - [i10]Simon Dufort-Labbé, Pierluca D'Oro, Evgenii Nikishin, Razvan Pascanu, Pierre-Luc Bacon, Aristide Baratin:
Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons. CoRR abs/2403.07688 (2024) - [i9]Zhixuan Lin, Pierluca D'Oro, Evgenii Nikishin, Aaron C. Courville:
The Curse of Diversity in Ensemble-Based Exploration. CoRR abs/2405.04342 (2024) - [i8]Nate Rahn, Pierluca D'Oro, Marc G. Bellemare:
Controlling Large Language Model Agents with Entropic Activation Steering. CoRR abs/2406.00244 (2024) - 2023
- [c6]Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G. Bellemare, Aaron C. Courville:
Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier. ICLR 2023 - [c5]Nate Rahn, Pierluca D'Oro, Harley Wiltzer, Pierre-Luc Bacon, Marc G. Bellemare:
Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control. NeurIPS 2023 - [i7]Nate Rahn, Pierluca D'Oro, Harley Wiltzer, Pierre-Luc Bacon, Marc G. Bellemare:
Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control. CoRR abs/2309.14597 (2023) - [i6]Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff:
Motif: Intrinsic Motivation from Artificial Intelligence Feedback. CoRR abs/2310.00166 (2023) - 2022
- [c4]Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron C. Courville:
The Primacy Bias in Deep Reinforcement Learning. ICML 2022: 16828-16847 - [i5]Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron C. Courville:
The Primacy Bias in Deep Reinforcement Learning. CoRR abs/2205.07802 (2022) - 2021
- [c3]Alberto Maria Metelli, Matteo Papini, Pierluca D'Oro, Marcello Restelli:
Policy Optimization as Online Learning with Mediator Feedback. AAAI 2021: 8958-8966 - 2020
- [c2]Pierluca D'Oro, Alberto Maria Metelli, Andrea Tirinzoni, Matteo Papini, Marcello Restelli:
Gradient-Aware Model-Based Policy Search. AAAI 2020: 3801-3808 - [c1]Pierluca D'Oro, Wojciech Jaskowski:
How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization. NeurIPS 2020 - [i4]Giorgio Giannone, Asha Anoosheh, Alessio Quaglino, Pierluca D'Oro, Marco Gallieri, Jonathan Masci:
Real-time Classification from Short Event-Camera Streams using Input-filtering Neural ODEs. CoRR abs/2004.03156 (2020) - [i3]Pierluca D'Oro, Wojciech Jaskowski:
How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization. CoRR abs/2004.14309 (2020) - [i2]Alberto Maria Metelli, Matteo Papini, Pierluca D'Oro, Marcello Restelli:
Policy Optimization as Online Learning with Mediator Feedback. CoRR abs/2012.08225 (2020)
2010 – 2019
- 2019
- [i1]Pierluca D'Oro, Alberto Maria Metelli, Andrea Tirinzoni, Matteo Papini, Marcello Restelli:
Gradient-Aware Model-based Policy Search. CoRR abs/1909.04115 (2019)
Coauthor Index
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