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This research proposes using action noise patterns derived from infant motor development to enhance exploration efficiency in deep reinforcement learning. By progressively increasing the temporal auto-correlation of agent behavior during training, this method achieves smoother trajectories and improved state-space coverage compared to standard white noise strategies. The findings demonstrate that biological developmental principles can offer concrete guidance for optimizing artificial intelligence learning mechanisms.
Infant Spontaneous Movement Noise Improves Exploration in Deep RL
arXiv:2606.16590v2 Announce Type: replace 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.
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 this https URL.
6 pages, 4 figures, 1 table. Accepted at IEEE ICDL 2026. Cite as: F. M. López, M. R. Ernst, F. Cruz, M. Hoffmann, and J. Triesch, "Infant Spontaneous Movement Noise Improves Exploration in Deep RL", in 2026 IEEE International Conference on Development and Learning (ICDL). IEEE, 2026, pp. 1-6
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC)
arXiv:2606.16590 [cs.LG]
(or arXiv:2606.16590v2 [cs.LG] for this version)
From: Francisco M. López [view email] [v1] Mon, 15 Jun 2026 11:35:06 UTC (1,672 KB) [v2] Tue, 16 Jun 2026 02:50:13 UTC (1,672 KB)
Full text under the publisher's open licence · 245 words
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