Topic: successor features

Learning and memory consolidation in the brain occur over multiple timescales. Inspired by this observation, it has been shown that catastrophic forgetting in reinforcement learning agents can be mitigated by consolidating Q-value function parameters at multiple timescales. In this work, we combine this approach with successor features, and show that by consolidating successor features and preferences learned over multiple timescales we can further mitigate catastrophic forgetting. In particular, we show that agents trained with this approach rapidly recall previously rewarding sites in large