For FC-individual, we train each testing assay separately with a two-layer fully-connected base learner. For FC-All, a two-layer fully connected model is trained on samples from both support setandquery setofsource assays andfrom thesupport setofthetargetassay. C.1 DrugActivityPredictionData For drug activity prediction, here we summarized the number of assays belonging to each target family: GPCR (685), Ion channel (215), Kinase (665), NHR (123), Binding (2523), Phenotypic (2299), Functional (1689), Proteinase (289),ADME (55).

The compositionality of the model improves the capacity of generalization tovarious andevenout-of-distribution tasks.

We have shown experimentally that our method is effective in a variety of domains; however, other problem domains may require additional hyperparameter tuning, which can be expensive.

However, in domains where precise and succinct expert state information is available, agents trained onsuchexpert state features usually outperform agents trained onrichobservations.

Kernel matrix-vector multiplication (KMVM) is one of the most important operations needed in scientific computing with core applications indiffeomorphic registration, geometric learning [11], [31],numerical analysis [28],fluid dynamics [6],and machine learning [27].

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Incontrast,our work is concerned with an overall limit on the total amount of information an agent may acquire fromtheenvironment and,inturn,howthattranslates intoitsselection ofafeasible learning target.

Inthiswork,weconsider thescenario where agent limitations may entirely preclude identifying an exactly value-equivalent model, immediately giving rise to a trade-off between identifying a model that is simple enough to learn while only incurring bounded sub-optimality.

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