Estimating Maximum Expected Value through Gaussian Approximation. - Carlo D'Eramo; Marcello Restelli; Alessandro Nuara (ICML, 2016)

Estimating the Maximum Expected Value in Continuous Reinforcement Learning Problems. - Carlo D'Eramo; Alessandro Nuara; Matteo Pirotta; Marcello Restelli (AAAI, 2017)

Boosted Fitted Q-Iteration. - Samuele Tosatto; Matteo Pirotta; Carlo D'Eramo; Marcello Restelli (ICML, 2017)

Exploiting structure and uncertainty of Bellman updates in Markov decision processes. - Davide Tateo; Carlo D'Eramo; Alessandro Nuara; Marcello Restelli; Andrea Bonarini (SSCI, 2017)

Exploiting Action-Value Uncertainty to Drive Exploration in Reinforcement Learning. - Carlo D'Eramo; Andrea Cini; Marcello Restelli (IJCNN, 2019)

Exploration Driven by an Optimistic Bellman Equation. - Samuele Tosatto; Carlo D'Eramo; Joni Pajarinen; Marcello Restelli; Jan Peters 001 (IJCNN, 2019)

On the exploitation of uncertainty to improve Bellman updates and exploration in Reinforcement Learning. - Carlo D'Eramo (2019)

Sharing Knowledge in Multi-Task Deep Reinforcement Learning. - Carlo D'Eramo; Davide Tateo; Andrea Bonarini; Marcello Restelli; Jan Peters 001 (ICLR, 2020)

Generalized Mean Estimation in Monte-Carlo Tree Search. - Tuan Dam; Pascal Klink; Carlo D'Eramo; Jan Peters 001; Joni Pajarinen (IJCAI, 2020)

Self-Paced Deep Reinforcement Learning. - Pascal Klink; Carlo D'Eramo; Jan Peters 001; Joni Pajarinen (NeurIPS, 2020)

Deep Reinforcement Learning with Weighted Q-Learning. - Andrea Cini; Carlo D'Eramo; Jan Peters 001; Cesare Alippi (CoRR, 2020)

Multi-Channel Interactive Reinforcement Learning for Sequential Tasks. - Dorothea Koert; Maximilian Kircher; Vildan Salikutluk; Carlo D'Eramo; Jan Peters 001 (Frontiers Robotics AI, 2020)

Convex Regularization in Monte-Carlo Tree Search. - Tuan Dam; Carlo D'Eramo; Jan Peters 001; Joni Pajarinen (ICML, 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 001 (ICRA, 2021)

Gaussian Approximation for Bias Reduction in Q-Learning. - Carlo D'Eramo; Andrea Cini; Alessandro Nuara; Matteo Pirotta; Cesare Alippi; Jan Peters 001; Marcello Restelli (J. Mach. Learn. Res., 2021)

MushroomRL: Simplifying Reinforcement Learning Research. - Carlo D'Eramo; Davide Tateo; Andrea Bonarini; Marcello Restelli; Jan Peters 001 (J. Mach. Learn. Res., 2021)

A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning. - Pascal Klink; Hany Abdulsamad; Boris Belousov; Carlo D'Eramo; Jan Peters 001; Joni Pajarinen (J. Mach. Learn. Res., 2021)

Boosted Curriculum Reinforcement Learning. - Pascal Klink; Carlo D'Eramo; Jan Peters 001; Joni Pajarinen (ICLR, 2022)

Curriculum Reinforcement Learning via Constrained Optimal Transport. - Pascal Klink; Haoyi Yang; Carlo D'Eramo; Jan Peters 001; Joni Pajarinen (ICML, 2022)

Prioritized Sampling with Intrinsic Motivation in Multi-Task Reinforcement Learning. - Carlo D'Eramo; Georgia Chalvatzaki (IJCNN, 2022)

Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning. - Simone Parisi; Davide Tateo; Maximilian Hensel; Carlo D'Eramo; Jan Peters 001; Joni Pajarinen (Algorithms, 2022)

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)

Composable energy policies for reactive motion generation and reinforcement learning. - Julen Urain; Anqi Li 001; Puze Liu; Carlo D'Eramo; Jan Peters 001 (Int. J. Robotics Res., 2023)

Parameterized Projected Bellman Operator. - Théo Vincent; Alberto Maria Metelli; Boris Belousov; Jan Peters 001; Marcello Restelli; Carlo D'Eramo (AAAI, 2024)

Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts. - Ahmed Hendawy; Jan Peters 001; Carlo D'Eramo (ICLR, 2024)

Augmented Bayesian Policy Search. - Mahdi Kallel; Debabrota Basu; Riad Akrour; Carlo D'Eramo (ICLR, 2024)

Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula. - Aryaman Reddi; Maximilian Tölle; Jan Peters 001; Georgia Chalvatzaki; Carlo D'Eramo (ICLR, 2024)

Domain Randomization via Entropy Maximization. - Gabriele Tiboni; Pascal Klink; Jan Peters 001; Tatiana Tommasi; Carlo D'Eramo; Georgia Chalvatzaki (ICLR, 2024)

Contact Energy Based Hindsight Experience Prioritization. - Erdi Sayar; Zhenshan Bing; Carlo D'Eramo; Ozgur S. Oguz; Alois Knoll (ICRA, 2024)

Uli-RL: A Real-World Deep Reinforcement Learning Pedagogical Agent for Children. - Anna Riedmann; Julia Götz; Carlo D'Eramo; Birgit Lugrin (KI, 2024)

Cyclicity-Regularized Coordination Graphs. - Oliver Järnefelt; Mahdi Kallel; Carlo D'Eramo (RLJ, 2024)

Iterated Q-Network: Beyond the One-Step Bellman Operator. - Théo Vincent; Daniel Palenicek; Boris Belousov; Jan Peters 001; Carlo D'Eramo (CoRR, 2024)

Deterministic Exploration via Stationary Bellman Error Maximization. - Sebastian Griesbach; Carlo D'Eramo (CoRR, 2024)

A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search. - Tuan Dam; Carlo D'Eramo; Jan Peters 001; Joni Pajarinen (J. Artif. Intell. Res., 2024)

On the Benefit of Optimal Transport for Curriculum Reinforcement Learning. - Pascal Klink; Carlo D'Eramo; Jan Peters 001; Joni Pajarinen (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning. - Théo Vincent; Fabian Wahren; Jan Peters 001; Boris Belousov; Carlo D'Eramo (ICLR, 2025)

Real-World Testing Matters in Reinforcement Learning for Education. - Anna Riedmann; Carlo D'Eramo; Birgit Lugrin (AAMAS, 2025)

Iterated Q-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning. - Théo Vincent; Daniel Palenicek; Boris Belousov; Jan Peters 001; Carlo D'Eramo (Trans. Mach. Learn. Res., 2025)

Machine Learning with Physics Knowledge for Prediction: A Survey. - Joe Watson; Chen Song; Oliver Weeger; Theo Gruner; An Thai Le 001; Kay Hansel; Ahmed Hendawy; Oleg Arenz; Will Trojak; Miles D. Cranmer; Carlo D'Eramo; Fabian Bülow; et al. (Trans. Mach. Learn. Res., 2025)

Continual Learning Should Move Beyond Incremental Classification. - Rupert Mitchell; Antonio Alliegro; Raffaello Camoriano; Dustin Carrión-Ojeda; Antonio Carta; Georgia Chalvatzaki; Nikhil Churamani; Carlo D'Eramo; Samin Hamidi; Robin Hesse; Fabian Hinder; Roshni Kamath; et al. (CoRR, 2025)

Eau De Q-Network: Adaptive Distillation of Neural Networks in Deep Reinforcement Learning. - Théo Vincent; Tim Lukas Faust; Yogesh Tripathi; Jan Peters 001; Carlo D'Eramo (CoRR, 2025)

Dynamic Obstacle Avoidance with Bounded Rationality Adversarial Reinforcement Learning. - Jose Luis Holgado Alvarez; Aryaman Reddi; Carlo D'Eramo (CoRR, 2025)

Bridging the Performance Gap Between Target-Free and Target-Based Reinforcement Learning With Iterated Q-Learning. - Théo Vincent; Yogesh Tripathi; Tim Lukas Faust; Yaniv Oren; Jan Peters 001; Carlo D'Eramo (CoRR, 2025)

Learning to Explore in Diverse Reward Settings via Temporal-Difference-Error Maximization. - Sebastian Griesbach; Carlo D'Eramo (CoRR, 2025)