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Md Mirajul Islam

My research centers on modeling complex human decision-making systems using reinforcement learning (RL) and inverse reinforcement learning (IRL). These approaches allow for the formulation of dynamic, adaptive models that mimic or infer the underlying strategies individuals use in various domains. Specifically, I focus on multi-agent systems, where multiple decision-makers interact, often with conflicting goals. Within this framework, I incorporate optimization techniques to fine-tune policies and actions, ensuring efficient outcomes in a shared environment. Additionally, I integrate causal inference to unravel the underlying factors driving decision-making processes. By understanding these causal relationships, I can model not only the actions taken by agents but also how their interactions and feedback loops influence the overall system. My work has applications in domains such as education and healthcare, where decision-making is crucial and outcomes are sensitive to the interplay of multiple agents. Through this combination of RL, IRL, optimization, and causal inference, I aim to develop robust, interpretable models for complex decision environments.
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