Robot Learning

Successor Features

Successor features are a reinforcement learning representation (Barreto et al., 2017) that expresses a policy's value as the dot product between the expected discounted sum of future state features and a reward weight vector, decoupling environment dynamics from task reward. They generalize Dayan's successor representation (1993) and enable rapid transfer: when the reward changes but dynamics do not, new values follow from re-weighting cached successor features, supporting generalized policy improvement across task families.

Why it matters for physical AI

Robots amortize hardware cost by performing many tasks in one environment; representations that reuse dynamics knowledge across reward specifications directly serve that multi-task economics.