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Emotion-Inspired Learning Signals (EILS): A Homeostatic Framework for Adaptive Autonomous Agents

Dhruv Tiwari

TL;DR

This paper proposes that the unaddressed factor in robust autonomy is a functional analog to biological emotion, serving as a high-level homeostatic control mechanism, and introduces Emotion-Inspired Learning Signals (EILS), a unified framework that replaces scattered optimization heuristics with a coherent, bio-inspired internal feedback engine.

Abstract

The ruling method in modern Artificial Intelligence spanning from Deep Reinforcement Learning (DRL) to Large Language Models (LLMs) relies on a surge of static, externally defined reward functions. While this "extrinsic maximization" approach has rendered superhuman performance in closed, stationary fields, it produces agents that are fragile in open-ended, real-world environments. Standard agents lack internal autonomy: they struggle to explore without dense feedback, fail to adapt to distribution shifts (non-stationarity), and require extensive manual tuning of static hyperparameters. This paper proposes that the unaddressed factor in robust autonomy is a functional analog to biological emotion, serving as a high-level homeostatic control mechanism. We introduce Emotion-Inspired Learning Signals (EILS), a unified framework that replaces scattered optimization heuristics with a coherent, bio-inspired internal feedback engine. Unlike traditional methods that treat emotions as semantic labels, EILS models them as continuous, homeostatic appraisal signals such as Curiosity, Stress, and Confidence. We formalize these signals as vector-valued internal states derived from interaction history. These states dynamically modulate the agent's optimization landscape in real time: curiosity regulates entropy to prevent mode collapse, stress modulates plasticity to overcome inactivity, and confidence adapts trust regions to stabilize convergence. We hypothesize that this closed-loop homeostatic regulation can enable EILS agents to outperform standard baselines in terms of sample efficiency and non-stationary adaptation.

Emotion-Inspired Learning Signals (EILS): A Homeostatic Framework for Adaptive Autonomous Agents

TL;DR

This paper proposes that the unaddressed factor in robust autonomy is a functional analog to biological emotion, serving as a high-level homeostatic control mechanism, and introduces Emotion-Inspired Learning Signals (EILS), a unified framework that replaces scattered optimization heuristics with a coherent, bio-inspired internal feedback engine.

Abstract

The ruling method in modern Artificial Intelligence spanning from Deep Reinforcement Learning (DRL) to Large Language Models (LLMs) relies on a surge of static, externally defined reward functions. While this "extrinsic maximization" approach has rendered superhuman performance in closed, stationary fields, it produces agents that are fragile in open-ended, real-world environments. Standard agents lack internal autonomy: they struggle to explore without dense feedback, fail to adapt to distribution shifts (non-stationarity), and require extensive manual tuning of static hyperparameters. This paper proposes that the unaddressed factor in robust autonomy is a functional analog to biological emotion, serving as a high-level homeostatic control mechanism. We introduce Emotion-Inspired Learning Signals (EILS), a unified framework that replaces scattered optimization heuristics with a coherent, bio-inspired internal feedback engine. Unlike traditional methods that treat emotions as semantic labels, EILS models them as continuous, homeostatic appraisal signals such as Curiosity, Stress, and Confidence. We formalize these signals as vector-valued internal states derived from interaction history. These states dynamically modulate the agent's optimization landscape in real time: curiosity regulates entropy to prevent mode collapse, stress modulates plasticity to overcome inactivity, and confidence adapts trust regions to stabilize convergence. We hypothesize that this closed-loop homeostatic regulation can enable EILS agents to outperform standard baselines in terms of sample efficiency and non-stationary adaptation.
Paper Structure (48 sections, 10 equations, 3 figures, 2 tables, 1 algorithm)

This paper contains 48 sections, 10 equations, 3 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. The Frozen Synapse Problem
  3. The Alignment Gap in Dynamic Environments
  4. The Stability-Plasticity Dilemma
  5. The Homeostatic Hypothesis
  6. Biological Foundations & Related Work
  7. Biological Foundations
  8. The Locus Coeruleus and Norepinephrine (Stress)
  9. The Anterior Cingulate Cortex (Confidence)
  10. Active Inference (Curiosity)
  11. The Algorithmic Landscape
  12. Intrinsic Motivation and Exploration
  13. Continual & Meta-Learning
  14. Evolutionary Strategies vs. Homeostasis
  15. Autonomous Agents and LLMs
  16. ...and 33 more sections

Figures (3)

  • Figure 1: Recovery Analysis (EILS Agent): A 3-panel diagnostic of the agent's response to the "Frozen Synapse" test. Top: Total Reward recovers after the gravity shift (Red Dashed Line). Middle: Internal Stress ($\sigma_t$) accumulates (Orange line) as prediction errors spike. Bottom: The Plasticity Response acts without delay, boosting the Learning Rate (Green line) by $600\%$ to enable rapid adaptation.
  • Figure 2: Exploration Heatmaps: A comparison of state visitation density. Left: Standard PPO gets stranded in a local "safety loop" (dark region). Right: EILS demonstrates high-entropy coverage (bright region), visiting $88\%$ of valid states due to the Curiosity ($\kappa_t$) drive.
  • Figure 3: The Panic Signature: A zoomed-in view of the biological control loop at Episode 500. Note the stepwise spike in Frustration/Stress (Orange, Top) immediately following the environment shift (Red Dashed Line). This directly initiates the Plasticity/Learning Rate increase (Green, Bottom), confirming the causal homeostatic mechanism.