Evolutionary Discovery of Developmental Reward Schedules in Deep Reinforcement Learning
The temporal structure of reward composition in reinforcement learning (RL) is typically hand-designed and held fixed throughout training, leaving the progression of motivational priorities largely unexplored. In this work, we propose an evolutionary framework for discovering developmental reward schedules, in which three distinct biologically inspired motivational components -- agency, novelty, and reactivity -- are combined through time-varying weights that dynamically shift over the course of training. Evaluated on two sparse-reward MiniGrid tasks: DoorKey-6x6 and KeyCorridorS3R1, our framework compares the generalizability of four evolutionary algorithms: CMA-ES, xNES, DE, and L-SHADE...
| Authors | Alan Nadelsticher Ruvalcaba |
|---|---|
| Primary category | cs.LG |
| Published | 18 June 2026 |
| Last updated at source | 10 July 2026 |
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