Infant Spontaneous Movement Noise Improves Exploration in Deep RL
2026-06-15 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors looked at how babies move spontaneously and found that their movements have a special pattern of changes over time, called colored noise, which gets more consistent as they grow. They used this idea to make a new way for artificial intelligence (AI) programs that learn by trying actions to explore more smoothly and effectively. By gradually increasing how connected the 'action noise' is over time in training, their method helped AI learn better in several test environments compared to usual random noise methods. This suggests that studying how humans develop can help improve AI learning strategies.
Deep Reinforcement LearningExploration NoiseColored NoiseTemporal Auto-correlationSpontaneous Infant MovementsPower Spectral DensityState Space CoverageLearning EfficiencyAction Noise
Authors
Francisco M. López, Markus R. Ernst, Francisco Cruz, Matej Hoffmann, and Jochen Triesch
Abstract
Exploration in deep reinforcement learning (RL) is commonly implemented as temporally uncorrelated white noise. However, recent works show that temporally correlated colored noise can improve exploration efficiency by producing smooth trajectories with better coverage of the state space. We inquire whether action noise inspired by infant spontaneous movements can also improve exploration in deep RL. We find that the power spectral densities of babies' end-effector velocities follow a colored noise process where the spectral exponent increases with age. Inspired by this developmental pattern, we introduce a mechanism that progressively increases the temporal auto-correlation of exploration noise during RL training, matching the infant statistics. Experiments across several RL environments show that infant-inspired noise produces structured exploratory behavior and can improve learning efficiency compared to conventional exploration strategies. These findings suggest that human motor and cognitive development can provide useful guidance for designing learning mechanisms in artificial agents. Our code is available at https://github.com/trieschlab/baby-noise-rl.