Developmental Curiosity and Social Interaction in Virtual Agents
On the map: World models → Latent dynamics models and model-based RL
Abstract (arXiv)
Infants explore their complex physical and social environment in an organized way. To gain insight into what intrinsic motivations may help structure this exploration, we create a virtual infant agent and place it in a developmentally-inspired 3D environment with no external rewards. The environment has a virtual caregiver agent with the capability to interact contingently with the infant agent in ways that resemble play. We test intrinsic reward functions that are similar to motivations that have been proposed to drive exploration in humans: surprise, uncertainty, novelty, and learning progress. These generic reward functions lead the infant agent to explore its environment and discover the contingencies that are embedded into the caregiver agent. The reward functions that are proxies for novelty and uncertainty are the most successful in generating diverse experiences and activating the environment contingencies. We also find that learning a world model in the presence of an attentive caregiver helps the infant agent learn how to predict scenarios with challenging social and physical dynamics. Taken together, our findings provide insight into how curiosity-like intrinsic rewards and contingent social interaction lead to dynamic social behavior and the creation of a robust predictive world model.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Latent dynamics models and model-based RL, one of 11 directions in World models. Builds on 13 of its 35 references that are in the map. First posted on arXiv 22 May 2023.
Builds on
13 of 35 references.
Closest ancestors · 2
Same direction, newest first
- 2020Planning to Explore via Self-Supervised World Models12 May
- 2018Learning to Play with Intrinsically-Motivated Self-Aware Agents21 Feb
Other references · 11
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,173
- 2018Exploration by Random Network Distillationcited by 966
- 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 503
- 2016#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learningcited by 307
- 2019Self-Supervised Exploration via Disagreementcited by 263
- 2017Automated Curriculum Learning for Neural Networkscited by 251
- 2018Unity: A General Platform for Intelligent Agentscited by 232
- 2016A Compositional Object-Based Approach to Learning Physical Dynamicscited by 119
- 2017Surprise-Based Intrinsic Motivation for Deep Reinforcement Learningcited by 83
- 2020Active World Model Learning with Progress Curiositycited by 27
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Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2018Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation
- 2018Computational Theories of Curiosity-Driven Learning
- 2025From Curiosity to Competence: How World Models Interact with the Dynamics of Exploration
- 2025SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models
- 2026Intrinsic Motivation in Reinforcement Learning: A Research Agenda for Adaptive Self-Organisation
- 2024The intrinsic motivation of reinforcement and imitation learning for sequential tasks
- 2025Intrinsic Goals for Autonomous Agents: Model-Based Exploration in Virtual Zebrafish Predicts Ethological Behavior and Whole-Brain Dynamics
- 2022Toddler-Guidance Learning: Impacts of Critical Period on Multimodal AI Agents