NeurIPS 2024 Past Other
Intrinsically-Motivated and Open-Ended Learning Workshop @NeurIPS2024
NeurIPS 2024 Workshop IMOL
- Submission deadline
- Sep 17, 2024, 12:00 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (47)
Fetched from OpenReview (v2) on 2026-06-10.
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A meta unit for co-constructing a computational scaffold model to guide human motor learning
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A Multi-agent Reinforcement Learning Study of Evolution of Communication and Teaching under Libertarian and Utilitarian Governing Systems
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A role for phasic serotonergic signaling in regulating augmentations during open-ended learning
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A Single Goal is All You Need
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Autotelic LLM-based exploration for goal-conditioned RL
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Bayesian Online Non-Stationary Detection for Robust Reinforcement Learning
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Bridging Natural Language and Emergent Representation in Hierarchical Reinforcement Learning
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Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learning
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CONCLAD: COntinuous Novel CLAss Detector
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Diversity Progress for Goal Selection in Discriminability-Motivated RL
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Does Infantile Attachment Require Intrinsic Reward?
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Dreaming Learning
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Emergence of Implicit World Models from Mortal Agents
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Empathic Coupling of Homeostatic States for Intrinsic Prosociality
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Empowerment and Causal Learning
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Enhanced Exploration via Variational Learned Priors
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Episodic Novelty Through Temporal Distance
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First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs
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Fostering Intrinsic Motivation in Reinforcement Learning with Pretrained Foundation Models
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From Laws to Motivation: Guiding Exploration through Law-Based Reasoning and Rewards
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Hierarchical Orchestra of Policies
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Implementing Human Information-Seeking Behaviour with Action-Agnostic Bayesian Surprise
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Incentivizing Exploration With Causal Curiosity as Intrinsic Motivation
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InfiniteKitchen: Cross-environment Cooperation for Zero-shot Multi-agent Coordination
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Knowledge Retention in Continual Model-Based Reinforcement Learning
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Local Ridge Regression Resets Mitigate Plasticity Loss
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Model-Agnostic Meta-Learning with Open-Ended Reinforcement Learning
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Modeling Cognitive Strategies in Teaching
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Modeling Goal Selection with Program Synthesis
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OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code
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Playful and Exploratory Behavior from the Maximum Occupancy Principle
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PreND: Enhancing Intrinsic Motivation in Reinforcement Learning through Pre-trained Network Distillation
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Prioritizing Compression Explains Human Perceptual Preferences
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Quality-Diversity Self-Play: Open-Ended Strategy Innovation via Foundation Models
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Representing Positional Information in Generative World Models for Object Manipulation
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SAC-GLAM: Improving Online RL for LLM agents with Soft Actor-Critic and Hindsight Relabeling
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Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement Learning
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Self-Efficacy Update in Reinforcement Learning: Impact on Goal Selection for Q-learning Agents
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SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models
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Skill Disentanglement in Reproducing Kernel Hilbert Space
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Testing causal hypotheses through Hierarchical Reinforcement Learning
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The Agent-Environment Boundary
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The Creative Act: Effective Exploration by Seeking Surprise
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Toward Universal and Interpretable World Models for Open-ended Learning Agents
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Unlocking New Strategies: Intrinsic Exploration for Evolving Macro and Micro Actions
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Using adaptive intrinsic motivation in RL to model learning across development
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We Urgently Need Intrinsically Kind Machines