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and interpreting learned agent behaviour will be investigated to support trust and adoption in industrial environments. The research aims to advance the state of the art in intelligent data-driven
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empirically grounded insights into the aesthetic, lived, and institutional realities of contemporary world literature and its agents. Combining textual analysis with sociological and ethnographic methods, EMLIT
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empirically grounded insights into the aesthetic, lived, and institutional realities of contemporary world literature and its agents. Combining textual analysis with sociological and ethnographic methods, EMLIT
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grounded insights into the aesthetic, lived, and institutional realities of contemporary world literature and its agents. Combining textual analysis with sociological and ethnographic methods, EMLIT aims
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of reinforcement learning agents understandable to humans, leading to improved transparency, trust, safety, and regulatory compliance in high-stakes decision-making systems. Potential application domains include
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actively on the preparation and defence of a PhD thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks
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thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks, environments, or objectives while retaining