This workshop aims to bridge highly active but largely parallel research communities, addressing the problem of generalizable and transferrable learning for all forms of sequential decision making (SDM), including reinforcement learning and AI planning. We expect that this workshop will play a key role in accelerating the speed of foundational innovation in SDM with a synthesis of the best ideas for learning generalizable representations of learned knowledge and for reliably utilizing the learned knowledge across different sequential decision-making problems. NeurIPS presents an ideal, inclusive venue for dialog and technical interaction among researchers spanning the vast range of research communities that focus on these topics.
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