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Embodiment-Induced Coordination Regimes in Tabular Multi-Agent Q-Learning

Muhammad Ahmed Atif, Nehal Naeem Haji, Mohammad Shahid Shaikh, Muhammad Ebad Atif

TL;DR

This paper interrogates the common assumption that centralized value learning universally improves coordination in multi-agent reinforcement learning. By conducting a controlled, fully tabular predator–prey study with explicit embodiment constraints (speed and stamina), it demonstrates that coordination benefits from centralization are not universal and depend critically on regime and role symmetry. The authors isolate coordination structure from representation learning and function approximation, revealing regime-specific patterns where independent learning can outperform centralized approaches and where asymmetric configurations induce persistent coordination breakdowns. These findings highlight the importance of embodiment-aware design in MARL and suggest that centralized coordination can become a liability under certain constraints, informing future research toward more nuanced coordination strategies.

Abstract

Centralized value learning is often assumed to improve coordination and stability in multi-agent reinforcement learning, yet this assumption is rarely tested under controlled conditions. We directly evaluate it in a fully tabular predator-prey gridworld by comparing independent and centralized Q-learning under explicit embodiment constraints on agent speed and stamina. Across multiple kinematic regimes and asymmetric agent roles, centralized learning fails to provide a consistent advantage and is frequently outperformed by fully independent learning, even under full observability and exact value estimation. Moreover, asymmetric centralized-independent configurations induce persistent coordination breakdowns rather than transient learning instability. By eliminating confounding effects from function approximation and representation learning, our tabular analysis isolates coordination structure as the primary driver of these effects. The results show that increased coordination can become a liability under embodiment constraints, and that the effectiveness of centralized learning is fundamentally regime and role dependent rather than universal.

Embodiment-Induced Coordination Regimes in Tabular Multi-Agent Q-Learning

TL;DR

This paper interrogates the common assumption that centralized value learning universally improves coordination in multi-agent reinforcement learning. By conducting a controlled, fully tabular predator–prey study with explicit embodiment constraints (speed and stamina), it demonstrates that coordination benefits from centralization are not universal and depend critically on regime and role symmetry. The authors isolate coordination structure from representation learning and function approximation, revealing regime-specific patterns where independent learning can outperform centralized approaches and where asymmetric configurations induce persistent coordination breakdowns. These findings highlight the importance of embodiment-aware design in MARL and suggest that centralized coordination can become a liability under certain constraints, informing future research toward more nuanced coordination strategies.

Abstract

Centralized value learning is often assumed to improve coordination and stability in multi-agent reinforcement learning, yet this assumption is rarely tested under controlled conditions. We directly evaluate it in a fully tabular predator-prey gridworld by comparing independent and centralized Q-learning under explicit embodiment constraints on agent speed and stamina. Across multiple kinematic regimes and asymmetric agent roles, centralized learning fails to provide a consistent advantage and is frequently outperformed by fully independent learning, even under full observability and exact value estimation. Moreover, asymmetric centralized-independent configurations induce persistent coordination breakdowns rather than transient learning instability. By eliminating confounding effects from function approximation and representation learning, our tabular analysis isolates coordination structure as the primary driver of these effects. The results show that increased coordination can become a liability under embodiment constraints, and that the effectiveness of centralized learning is fundamentally regime and role dependent rather than universal.
Paper Structure (27 sections, 4 equations, 7 figures, 4 tables, 2 algorithms)

This paper contains 27 sections, 4 equations, 7 figures, 4 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Background and Overview
  4. Game-Theoretic Models for Multi-Agent Interaction
  5. Stochastic Games
  6. Single-Agent RL Reductions in Multi-Agent Settings
  7. Independent Q-Learning (IQL)
  8. Centralized Q-Learning (CQL)
  9. Experimental Setup
  10. Environment and State Representation
  11. Action Model and Embodiment Constraints
  12. Learning Algorithms
  13. Experimental Conditions
  14. Training Protocol
  15. Evaluation Metrics
  16. ...and 12 more sections

Figures (7)

  • Figure 1: Mean episode length over training under the base-speed regime. Curves show the mean across 10 independent seeds. Lower values correspond to more efficient predator coordination.
  • Figure 2: Mean predator reward over training under the base-speed regime. Curves show the mean across 10 independent seeds. Higher values correspond to faster and more efficient capture behavior.
  • Figure 3: Mean prey reward over training under the base-speed regime. Curves show the mean across 10 independent seeds. Higher rewards correspond to longer survival times.
  • Figure 4: Mean episode length over training under the predator-speed-advantage regime. Curves show the mean across 10 independent seeds. Lower values correspond to more efficient predator coordination.
  • Figure 5: Mean predator reward over training under the predator-speed-advantage regime. Curves show the mean across 10 independent seeds. Higher rewards reflect improved exploitation of kinematic advantage through coordination.
  • ...and 2 more figures