Publications

(2024). Synthesis of Hierarchical Controllers Based on Deep Reinforcement Learning Policies. arXiv Preprint.

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(2023). WAE-PCN: Wasserstein-autoencoded Pareto Conditioned Networks. Proceedings of the Adaptive and Learning Agents Workshop (ALA 2023).

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(2023). The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models. To appear at The Twelfth International Conference on Learning Representations, ICLR 2024.

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(2023). Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees. The Eleventh International Conference on Learning Representations, ICLR 2023.

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(2022). Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes. Proceedings of the AAAI Conference on Artificial Intelligence Vol. 36 No. 6: AAAI-22 Technical Tracks 6, 6497-6505.

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(2022). A Framework for Flexibly Guiding Learning Agents. Neural Computing and Applications, Special Issue on Adaptive and Learning Agents 2021.

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(2020). Life is Random, Time is Not: Markov Decision Processes with Window Objectives. Logical Methods in Computer Science, December 14, 2020, Volume 16, Issue 4.

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(2020). Simple Strategies in Multi-Objective MDPs. Tools and Algorithms for the Construction and Analysis of Systems - 26th International Conference, TACAS 2020, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020, Dublin, Ireland, April 25-30, 2020, Proceedings, Part I.

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(2019). Life Is Random, Time Is Not: Markov Decision Processes with Window Objectives. 30th International Conference on Concurrency Theory, CONCUR 2019, August 27-30, 2019, Amsterdam, the Netherlands.

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