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The Option Keyboard (OK) was recently proposed as a method for transferringbehavioral knowledge across tasks. OK transfers knowledge by adaptivelycombining subsets of known behaviors using Successor Features (SFs) andGeneralized Policy Improvement (GPI). However, it relies on hand-designedstate-features and task encodings which are cumbersome to design for every newenvironment. In this work, we propose the "Successor Features Keyboard" (SFK),which enables transfer with discovered state-features and task encodings. Toenable discovery, we propose the "Categorical Successor Feature Approximator"(CSFA), a novel learning algorithm for estimating SFs while jointly discoveringstate-features and task encodings. With SFK and CSFA, we achieve the firstdemonstration of transfer with SFs in a challenging 3D environment where allthe necessary representations are discovered. We first compare CSFA againstother methods for approximating SFs and show that only CSFA discoversrepresentations compatible with SF&GPI at this scale. We then compare SFKagainst transfer learning baselines and show that it transfers most quickly tolong-horizon tasks.

Combining Behaviors with the Successor Features Keyboard