Evolutionary optimization discovers developmental reward schedules that improve performance over extrinsic-only baselines on some MiniGrid tasks, with novelty emerging as the dominant early signal.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
MSC-CMA-ES makes CMA-ES restarts structure-aware via cyclic nearest-better basin discovery on Sobol pre-samples, achieving 2.7x higher target coverage than BIPOP-CMA-ES on composition functions across CEC suites.
citing papers explorer
-
Evolutionary Discovery of Developmental Reward Schedules in Deep Reinforcement Learning
Evolutionary optimization discovers developmental reward schedules that improve performance over extrinsic-only baselines on some MiniGrid tasks, with novelty emerging as the dominant early signal.
-
MSC-CMA-ES: Structure-Aware Restarts for CMA-ES via Cyclic Nearest-Better Basin Discovery
MSC-CMA-ES makes CMA-ES restarts structure-aware via cyclic nearest-better basin discovery on Sobol pre-samples, achieving 2.7x higher target coverage than BIPOP-CMA-ES on composition functions across CEC suites.