mlsanityData to 14 Oct 2026

Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning

arXiv 2403.06880 ↗First posted 11 Mar 2024cs.LG cs.AIcited by 2

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

Toddlers evolve from free exploration with sparse feedback to exploiting prior experiences for goal-directed learning with denser rewards. Drawing inspiration from this Toddler-Inspired Reward Transition, we set out to explore the implications of varying reward transitions when incorporated into Reinforcement Learning (RL) tasks. Central to our inquiry is the transition from sparse to potential-based dense rewards, which share optimal strategies regardless of reward changes. Through various experiments, including those in egocentric navigation and robotic arm manipulation tasks, we found that proper reward transitions significantly influence sample efficiency and success rates. Of particular note is the efficacy of the toddler-inspired Sparse-to-Dense (S2D) transition. Beyond these performance metrics, using Cross-Density Visualizer technique, we observed that transitions, especially the S2D, smooth the policy loss landscape, promoting wide minima that enhance generalization in RL models.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 29 of its 50 references that are in the map. First posted on arXiv 11 Mar 2024.

Builds on

29 of 50 references.

References · 29

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17 more
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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agentscited by 6
  2. 2025From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learningcited by 3

Similar, not cited

Close in wording, no citation either way

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  3. 2025Emergence of Goal-Directed Behaviors via Active Inference with Self-Prior
  4. 2025DISCOVER: Automated Curricula for Sparse-Reward Reinforcement Learning
  5. 2025Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL
  6. 2026Evolutionary Discovery of Developmental Reward Schedules in Deep Reinforcement Learning
  7. 2026SUN: Reaching for Novelty in Reinforcement Learning
  8. 2026Understanding Goal Generalisation in Sequential Reinforcement Learning