While participants have a strong preference for using goals as an objective specification mechanism, a surprising result is found: even non-expert users are equally capable of specifying and interpreting reward functions.
It is suggested that the fundamental question of how to reasonably specify preferences is still not entirely settled, and some closely related, and oft-conflated, concepts that arise naturally whenever optimization is applied to robotics problems are explored.
Agent evaluations should be evaluated as configurable systems themselves, and that some desired behaviors are more effectively implemented in the system than requested through prompting, according to a new benchmark of four scientific tasks.
Luis Wiedmann, Leander Girrbach, Cordelia Schmid et al.· 0 citations
This work defines efficient explorers as agents that prioritize generating generalizable experience, i.e., data that supports learning models capable of predicting and adapting across the environment, and demonstrates that optimizing for these agents gives rise to an automatic curriculum of progressively more complex b...
Mikel Malagón, Jon Vadillo, Josu Ceberio et al.· 0 citations
Understanding the objectives that drive agents'behavior is crucial in strategic environments, where multiple agents interact and adapt to one another. This challenge arises in domains such as cybersecurity, autonomous driving, and online markets, where agents'underlying incentives are typically unknown and must be infe...
Antoine Bergerault, Cyrille Kone, G. Ramponi· 0 citations
Intrinsic rewards are designed to guide exploration in reinforcement learning by assigning value to an agent's experience, for example through prediction error or learning progress. However, maximizing these rewards need not produce the most informative experience available. We propose a formal criterion for exploratio...
Scott Viteri, Laura Gomezjurado Gonzalez, Clark W. Barrett· 1 citation
Generalist robot policies and language-model agents provide complementary capabilities, but completing a task also requires adapting their use to the consequences of physical interaction. We study execution as a locus of adaptation: feedback guides the current action and supplies evidence for revising subsequent decisi...
He-Yang Yu, Hao-Min Li, Si-Hang Li et al.· 0 citations
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