Mirco Mutti

I'm a postdoctoral researcher at the Technion - Israel Intitute of Technology, where I work with Aviv Tamar in the Robot Learning Lab. Previously, I received my PhD at Politecnico di Milano advised by Marcello Restelli within the Artificial Intelligence and Robotics Lab.

My research interest is in reinforcement learning. My current research tackles generalization and meta RL while previous contributions focused on unsupervised RL or learning without rewards. More broadly, my aim is to advance theoretical understanding that can lead to successful application of reinforcement learning in the real world. These include the study of partial observability, RL with general utilities, RLHF and inverse RL among others.

Feel free to drop an email for inquiries and questions. You can find my (mostly) up to date CV below. Turn to my Scholar or Bluesky page to catch up with more recent agenda.

Email  /  CV  /  Google Scholar  /  Bluesky

• Our paper A theoretical framework for partially-observed reward states in RLHF has been accepted at ICLR 2025.
• Check out our new preprint Reward compatibility: A framework for inverse RL to read about our journey into the theoretical foundations of inverse reinforcement learning.
• Our paper How does inverse RL scale to large state spaces? A provably efficient approach has been accepted at NeurIPS 2024.
• Our paper The limits of pure exploration in POMDPs: When the observation entropy is enough has been accepted at brand-new RLC conference.
• Four papers accepted at ICML 2024! They cover meta RL, inverse RL, geometric active exploration, and pure exploration in POMDPs. See you in Vienna!
• I gave a talk at the VANDAL lab on Unsupervised reinforcement learning in Turin.
• Our paper Exploiting causal graph priors with posterior sampling for reinforcement learning has been accepted at ICLR 2024.
• My PhD thesis received an honorable mention for the best PhD thesis on artificial intelligence by the AIxIA
• Our paper on Persuading farsighted receivers in MDPs: the power of honesty has been accepted at NeurIPS 2023.
• I gave a talk at the ETH LAS group and AI Center on (Non)convex reinforcement learning in Zurich.
• Happy to announce I am joining the Technion as a postdoctoral researcher from September 2023. I will work on reinforcement learning from theory to practice with Aviv Tamar.
• I succesfully defended my PhD thesis on March 2023.
• Our paper A tale of sampling and estimation in discounted reinforcement learning has been accepted at AISTATS 2023 with an oral presentation.
• I have been invited to give a talk as a Rising stars in AI at KAUST.
• Our paper Provably efficient causal model-based reinforcement learning for systematic generalization has been accepted at AAAI 2023.
• Our paper Challenging common assumptions in convex reinforcement learning has been accepted at NeurIPS 2022
• I have served as co-program chair for EWRL 2022 we organized in Milan.
• Grateful that our paper on The importance of non-Markovianity in maximum state entropy exploration has been recognised with the outstanding paper award at ICML 2022.

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