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. Previosuly, 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.
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
| Aug 01, 2024 | 🎉 Directed Exploration in Reinforcement Learning from Linear Temporal Logic was accepted at EWRL 2024 |
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| 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 |
| Aug 19, 2022 | 🪧 SFP (previously called TempoRL) was published in TMLR |
selected publications
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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 -
Efficient Learning of High Level Plans from PlayIn International Conference on Robotics and Automation, 2023 -
SFP: State-free Priors for Exploration in Off-Policy Reinforcement LearningTransactions on Machine Learning Research, 2022 -
Planning from Pixels in Environments with Combinatorially Hard Search SpacesIn Advances in Neural Information Processing Systems, 2021
