Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila
Researchers trained embodied agents in a neuromechanical fruit fly digital twin using 20 intrinsic motivation objectives, without external reward. The agents were compared against whole-brain recordings from real flies on spontaneous locomotion and on decoding future turn direction from kinematics and neural activity. DIAYN, a hierarchical competency algorithm selecting from discrete motor primitives, best matched fly behavior and neural prospective coding, including individual variability in exploration and the sum-difference rule for interhemispheric encoding.
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Researchers trained embodied agents in a neuromechanical fruit fly digital twin using 20 intrinsic motivation objectives, without external reward. The agents were compared against whole-brain recordings from real flies on spontaneous locomotion and on decoding future turn direction from kinematics and neural activity. DIAYN, a hierarchical competency algorithm selecting from discrete motor primitives, best matched fly behavior and neural prospective coding, including individual variability in exploration and the sum-difference rule for interhemispheric encoding.
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Source claims not independently checked; results are from a simulation-based digital twin and may not generalize beyond the tethered-ball assay.
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Key findings from the stored brief
- DIAYN achieved the strongest joint behavioral and neural prospective coding performance relative to flies among 20 intrinsic motivation algorithms.
- Learned DIAYN policies closely captured individual variability in fly exploration strategies.
- DIAYN's latent activity closely captured the sum-difference rule for interhemispheric encoding of kinematics.
- The hierarchical policy selects from a discrete library of low-level motor primitives.
Source claims not independently checked; results are from a simulation-based digital twin and may not generalize beyond the tethered-ball assay.
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Why this may matter
The work proposes hierarchical intrinsically motivated agents as a framework for reverse-engineering animal autonomy, linking computational objectives to measured neural representations of future volitional action.
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Source claims not independently checked; results are from a simulation-based digital twin and may not generalize beyond the tethered-ball assay.
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