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About this paper
J. K. Terry, Nathaniel Grammel, Sanghyun Son, Benjamin Black, Aakriti Agrawal
The authors show how one shared policy can represent different optimal behaviors for agents with heterogeneous observation and action spaces. Agent-specific indicators distinguish agents with overlapping observations, while padding accommodates different tensor dimensions. In five image-based environments, inversion ranked best in three among searched configurations. Prospector demonstrated learning with heterogeneous spaces, not a general guarantee of training success.
Summary by alphaXiv.
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