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