Better Transfer Learning with Inferred Successor Maps

Dayan's SR [3] is well-suited for transfer learning in settings with fixed dynamics, as the decomposition ofthevaluefunction intorepresentations ofexpected outcomes (future stateoccupancies) andcorresponding rewards allowsustoquickly recompute values under newrewardsettings.