About Me
I am a fifth-year Ph.D. student in Computer Science at Brown University in the Intelligent Robot Lab advised by George Konidaris. Previously, I got a Bachelor’s degree in Electronic Engineering from Universidad Simon Bolivar, Caracas, Venezuela and a Master’s degree in Computer Science from Politecnico di Milano where I was fortunate to work with Marcello Restelli and Nicola Gatti at the AIRLAB.
Moreover, in Summer 2021, I interned at Amazon Alexa and worked in the Dialogue Research group with Maryam Fazel-Zarandi on applications of LLMs (Large Language Models) and RL in Task-oriented Dialog Systems.
Contact rrs at brown dot edu — Google Scholar — LinkedIn — Github
Research Interests
I am deeply interested in enabling decision-making agents to learn reusable knowledge. To that end, currently, I’m working on learning state abstractions for Reinforcement Learning and, more generally, MDPs, that allow agents to learn provably sound, abstract and simpler world models that the agent can use to generate plans for different tasks.
Furthermore, I’ve also worked at the intersection of Natural Language and RL, investigating how to communicate prior knowledge to RL agents through languages. From this endeavor, RLang, a formal language for RL, was born. This language is unambiguous and designed precisely for RL. RLang allows to communicate partial task-specific knowledge to RL agents in order to avoid tabula rasa learning. The RLang framework, also, opens up many exciting research questions in RL algorithm design, natural language understanding and symbol grounding.
Publications
Conferences
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R. Rodriguez-Sanchez*, B. Spiegel*, J. Wang, R. Patel, S. Tellex, G. Konidaris. RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents. International Conference on Machine Learning (ICML). Honolulu, Hawaii, 2023. [paper] [RLang.ai] [RLang package]
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A. Tirinzoni*, R. Rodriguez-Sanchez*, M. Restelli. Transfer of Value Functions via Variational Methods. Advances in Neural Information Processing Systems (NeurIPS), Montreal, Canada, 2018. [paper][poster][code]
Workshops
- R. Rodriguez-Sanchez, G. Konidaris. Learning Abstract World Models for Value-preserving Planning with Options. Generalization in Planning at NeurIPS 2023 (Contributed Talk) and LatinX in AI Workshop. New Orleans, LA.
[Abstract][Paper]
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R. Rodriguez-Sanchez, B. Spiegel, J. Wang, R. Patel, S. Tellex, G. Konidaris. RLang: A Declarative Language for Expressing Prior Knowledge for Reinforcement Learning. Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM) 2022. Providence, RI. [Paper] [Poster]
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R. Rodriguez-Sanchez*, R. Patel*, G. Konidaris. On the Relationship Between Structure in Natural Language and Models of Sequential Decision Processes. Language and Reinforcement Learning (LaReL) Workshop at the International Conference in Machine Learning (ICML) 2020. [paper] [video]
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A. Bagaria, S. Kim, A. Mazzetto, R. Rodriguez-Sanchez. Replication of a Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment. NeurIPS 2019 Replication Challenge. [paper]
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A. Tirinzoni, R. Rodriguez-Sanchez, M. Restelli. Transferring Value Functions via Variational Methods. European Workshop on Reinforcement Learning (EWRL) 2018. Lille, France. Oral. [EWRL 2018].