Offline Learning for Planning: A Summary. - Giorgio Angelotti; Nicolas Drougard; Caroline Ponzoni Carvalho Chanel (CoRR, 2020)

Exploitation vs Caution: Risk-sensitive Policies for Offline Learning. - Giorgio Angelotti; Nicolas Drougard; Caroline Ponzoni Carvalho Chanel (CoRR, 2021)

Exploiting Expert-guided Symmetry Detection in Markov Decision Processes. - Giorgio Angelotti; Nicolas Drougard; Caroline P. C. Chanel (CoRR, 2021)

Expert-guided Symmetry Detection in Markov Decision Processes. - Giorgio Angelotti; Nicolas Drougard; Caroline P. C. Chanel (ICAART (2), 2022)

Data Augmentation Through Expert-Guided Symmetry Detection to Improve Performance in Offline Reinforcement Learning. - Giorgio Angelotti; Nicolas Drougard; Caroline P. C. Chanel (ICAART (2), 2023)

Data-Efficient Offline Reinforcement Learning with Approximate Symmetries. - Giorgio Angelotti; Nicolas Drougard; Caroline P. C. Chanel (ICAART (Revised Selected Paper), 2023)

HyPE: Attention with Hyperbolic Biases for Relative Positional Encoding. - Giorgio Angelotti (CoRR, 2023)

Towards a more efficient computation of individual attribute and policy contribution for post-hoc explanation of cooperative multi-agent systems using Myerson values. - Giorgio Angelotti; Natalia Díaz Rodríguez (Knowl. Based Syst., 2023)

Advances in Risk-Aware Offline Reinforcement Learning: A Study of Data Augmentation, Explainability, and Policy Selection. (Contributions à l'apprentissage par renforcement hors ligne avec prise en compte du risque : étude sur l'augmentation des données, sur la sélection des politiques et sur l'explicabilité). - Giorgio Angelotti (2023)

Offline Risk-sensitive RL with Partial Observability to Enhance Performance in Human-Robot Teaming. - Giorgio Angelotti; Caroline P. C. Chanel; Adam Henrique Moreira Pinto; Christophe Lounis; Corentin Chauffaut; Nicolas Drougard (AAMAS, 2024)