Carlo D'Eramo

According to our database, Carlo D'Eramo authored at least 31 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Iterated Q-Network: Beyond the One-Step Bellman Operator.

Théo Vincent, Daniel Palenicek, Boris Belousov, Jan Peters, Carlo D'Eramo

CoRR, 2024

Parameterized Projected Bellman Operator.

Théo Vincent, Alberto Maria Metelli, Boris Belousov, Jan Peters, Marcello Restelli, Carlo D'Eramo

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Composable energy policies for reactive motion generation and reinforcement learning.

Julen Urain, Anqi Li, Puze Liu, Carlo D'Eramo, Jan Peters

Int. J. Robotics Res., September, 2023

Contact Energy Based Hindsight Experience Prioritization.

Erdi Sayar, Zhenshan Bing, Carlo D'Eramo, Ozgur S. Oguz, Alois Knoll

CoRR, 2023

Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts.

Ahmed Hendawy, Jan Peters, Carlo D'Eramo

CoRR, 2023

Domain Randomization via Entropy Maximization.

Gabriele Tiboni, Pascal Klink, Jan Peters, Tatiana Tommasi, Carlo D'Eramo, Georgia Chalvatzaki

CoRR, 2023

Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula.

Aryaman Reddi, Maximilian Tölle, Jan Peters, Georgia Chalvatzaki, Carlo D'Eramo

CoRR, 2023

On the Benefit of Optimal Transport for Curriculum Reinforcement Learning.

Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

CoRR, 2023

Monte-Carlo tree search with uncertainty propagation via optimal transport.

Tuan Dam, Pascal Stenger, Lukas Schneider, Joni Pajarinen, Carlo D'Eramo, Odalric-Ambrym Maillard

CoRR, 2023

2022

A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search.

Tuan Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen

CoRR, 2022

Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning.

Simone Parisi, Davide Tateo, Maximilian Hensel, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Algorithms, 2022

Prioritized Sampling with Intrinsic Motivation in Multi-Task Reinforcement Learning.

Carlo D'Eramo, Georgia Chalvatzaki

Proceedings of the International Joint Conference on Neural Networks, 2022

Curriculum Reinforcement Learning via Constrained Optimal Transport.

Pascal Klink, Haoyi Yang, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Proceedings of the International Conference on Machine Learning, 2022

Boosted Curriculum Reinforcement Learning.

Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning.

Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen

J. Mach. Learn. Res., 2021

MushroomRL: Simplifying Reinforcement Learning Research.

Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters

J. Mach. Learn. Res., 2021

Gaussian Approximation for Bias Reduction in Q-Learning.

Carlo D'Eramo, Andrea Cini, Alessandro Nuara, Matteo Pirotta, Cesare Alippi, Jan Peters, Marcello Restelli

J. Mach. Learn. Res., 2021

Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning.

Andrew S. Morgan, Daljeet Nandha, Georgia Chalvatzaki, Carlo D'Eramo, Aaron M. Dollar, Jan Peters

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

Convex Regularization in Monte-Carlo Tree Search.

Tuan Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Multi-Channel Interactive Reinforcement Learning for Sequential Tasks.

Dorothea Koert, Maximilian Kircher, Vildan Salikutluk, Carlo D'Eramo, Jan Peters

Frontiers Robotics AI, 2020

Deep Reinforcement Learning with Weighted Q-Learning.

Andrea Cini, Carlo D'Eramo, Jan Peters, Cesare Alippi

CoRR, 2020

Self-Paced Deep Reinforcement Learning.

Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Generalized Mean Estimation in Monte-Carlo Tree Search.

Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Sharing Knowledge in Multi-Task Deep Reinforcement Learning.

Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

On the exploitation of uncertainty to improve Bellman updates and exploration in Reinforcement Learning.

Carlo D'Eramo

PhD thesis, 2019

Exploration Driven by an Optimistic Bellman Equation.

Samuele Tosatto, Carlo D'Eramo, Joni Pajarinen, Marcello Restelli, Jan Peters

Proceedings of the International Joint Conference on Neural Networks, 2019

Exploiting Action-Value Uncertainty to Drive Exploration in Reinforcement Learning.

Carlo D'Eramo, Andrea Cini, Marcello Restelli

Proceedings of the International Joint Conference on Neural Networks, 2019

2017

Exploiting structure and uncertainty of Bellman updates in Markov decision processes.

Davide Tateo, Carlo D'Eramo, Alessandro Nuara, Marcello Restelli, Andrea Bonarini

Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Boosted Fitted Q-Iteration.

Samuele Tosatto, Matteo Pirotta, Carlo D'Eramo, Marcello Restelli

Proceedings of the 34th International Conference on Machine Learning, 2017

Estimating the Maximum Expected Value in Continuous Reinforcement Learning Problems.

Carlo D'Eramo, Alessandro Nuara, Matteo Pirotta, Marcello Restelli

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Estimating Maximum Expected Value through Gaussian Approximation.

Carlo D'Eramo, Marcello Restelli, Alessandro Nuara

Proceedings of the 33nd International Conference on Machine Learning, 2016