Researchers propose a new framework for transfer learning in Markov Decision Processes that uses side information to construct tighter uncertainty sets, leading to less conservative policies under environmental shifts. This approach improves sample efficiency and performance in target domains compared to existing methods, offering significant benefits for content creators dealing with dynamic environments or data scarcity.
Read the full article at arXiv stat.ML
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Ali NematiWritten by Ali
