arXiv AI
Intrinsic Motivation in Reinforcement Learning: A Research Agenda for Adaptive Self-Organisation
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arXiv AI
Oct 27
Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila
The study investigates how intrinsic motivation drives autonomous behavior in fruit flies by combining whole-brain recordings with a neuromechanical digital twin. It shows that a hierarchical policy selecting from low‑level motor primitives, trained with 20 intrinsic motivation algorithms, can reproduce fly locomotor statistics and internal representations of future actions. The DIAYN algorithm, which maximizes behavioral entropy and sensory separability, best matches fly behavior and neural coding, capturing individual variability and interhemispheric kinematic encoding.
arXiv AI
1d ago
Building World Models from Intrinsic Motivation
arXiv:2611.13113v1 Announce Type: new Abstract: In the absence of extrinsic rewards or task supervision, human behavior is guided by intrinsic motivation---neural signals that drive curiosity, creati...
Hugging Face Trending Papers
Oct 25
Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila
Animals have a remarkable capacity for autonomy---driven by intrinsic motivation---without external sensory cues or extrinsic objectives, yet the computational mechanisms behind this ability largely r...
arXiv AI
Oct 1
Survival is the Only Reward: Sustainable Self-Training Through Environment-Mediated Selection
arXiv:2601.12310v2 Announce Type: replace Abstract: Self-training systems often degenerate due to the lack of an external criterion for judging data quality, leading to reward hacking and semantic dr...
arXiv AI
Jul 10
Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning
arXiv:2607. 08647v1 Announce Type: cross Abstract: As autonomous agents are increasingly deployed across diverse operational contexts, aligning their behavior with human intent demands reward functions that remain robust to such changes rather than overfitting to any single environment.
arXiv AI
Oct 16
EvolvingBrain: An Adaptive Skill System for Multi-modal Agents Self-Evolution in Open Worlds
EvolvingBrain introduces an adaptive skill system that enables multi-modal agents to self‑evolve in open worlds. The system comprises Stratified Skill Self‑Evolution, which extracts procedural and problematic patterns from trajectories to build a multi‑granularity skill architecture, and State‑Aware Adaptive Execution, which uses structured memory to guide skill fusion based on the agent’s current state. Experiments on VAB‑Minecraft and DiscoveryWorld show improved success rates and generalization, with code available for public release.