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.
Exponential Natural Evolution Strategies,
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WA-ASNG augments ASNG with weight adaptation that maximizes an estimated update signal, showing improved results over PBIL and ASNG on binary problems with population sizes 25-100 and under noise.
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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.
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Weight Adaptation for Improving Parallel Performance of Adaptive Stochastic Natural Gradient
WA-ASNG augments ASNG with weight adaptation that maximizes an estimated update signal, showing improved results over PBIL and ASNG on binary problems with population sizes 25-100 and under noise.