Title: Safety in Reinforcement Learning

Abstract: Reinforcement Learning (RL) agents can solve general problems based on little to no knowledge of the underlying environment. These agents often learn through experience, using a trial-and-error strategy that can lead to practical innovations, but this randomized process might cause undesirable events. Safe RL studies how to make such agents more reliable and how to ensure they behave appropriately. In this talk, we discuss these issues from online settings, where the agent interacts directly with the environment, to offline settings, where the agent only has access to historical data. We present new RL methods that exploit different types of prior knowledge to provide safety and reliability. Exploiting such prior knowledge, we present reliable offline algorithms that can improve the policy using less data and online algorithms that comply with safety constraints while learning. Besides safety and reliability, we also touch on other challenges preventing the deployment of RL in the real world, such as partial observability, generalization, and high-dimensional data.

Vitae: Thiago D. Simão (he/him) is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). He completed his Ph.D. in the Algorithmics Group at Delft University of Technology under the supervision of Dr. Matthijs Spaan. Following his doctoral studies, he worked as a Postdoctoral Researcher in the Department of Software Science at Radboud University Nijmegen, collaborating with Dr. Nils Jansen. His research focuses on enhancing the reliability of AI techniques to enable safe deployment in real-world applications. Specifically, he is interested in safe reinforcement learning, ensuring AI systems meet performance guarantees while preventing catastrophic failures. Through his work, he contributed to the advancement of trustworthy AI solutions in complex and uncertain environments.

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