Marco Bagatella
PhD student in RL, currently at the Learning and Adaptive Systems group.
bio
I am a PhD student at MPI-IS and ETH Zürich, co-advised by Georg Martius and Andreas Krause. I am mainly interested in (deep) reinforcement learning. Previously, I was fortunate to spend some time at the AIT Lab (ETH Zürich), under the supervision of Prof. Otmar Hilliges, and in the Autonomous Learning Group (MPI IS Tübingen).
Not so long ago, I obtained a MSc in Computer Science at ETH Zürich, and a few years before that I graduated from Politecnico di Milano (BSc in Engineering of Computing Systems).
research
My current main focus is on (deep) reinforcement learning, and I enjoy researching or learning about any related idea. In particular, my interests include exploration, temporal abstraction and offline approaches. In the long term, I aim to contribute to the understanding of current RL methods, and I am really excited about finding ways to tackle to rich, open-ended environments.
Outside of RL, I have some experience in representation learning and causality, and I like keeping an eye open towards other topics in machine learning or robotics.
news
| May 01, 2025 | 🎉 Active Fine-Tuning of Multi-task Policies and Zero-Shot Offline Imitation Learning via Optimal Transport were accepted at ICML 2025. |
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| Aug 01, 2024 | 🎉 Directed Exploration in Reinforcement Learning from Linear Temporal Logic was accepted at EWRL 2024 |
| Jul 22, 2024 | 🪧 Two papers I was involved in were presented at ICML 2024 |
| Sep 22, 2023 | 🎉 Goal-conditioned Offline Planning from Curious Exploration was accepted to NeurIPS 2023 |
| May 31, 2023 | 🪧 Efficient Learning of High Level Plans from Play was presented at ICRA 2023 |
selected publications
- Active Fine-Tuning of Generalist PoliciesIn Forty-second International Conference on Machine Learning, 2025
- Zero-shot Offline Imitation Learning via Optimal TransportIn Forty-second International Conference on Machine Learning, 2025
- Causal Action Influence Aware Counterfactual Data AugmentationIn Forty-first International Conference on Machine Learning, 2024
- Goal-conditioned Offline Planning from Curious ExplorationIn Advances in Neural Information Processing Systems, 2023