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Asynchronous Credit Assignment for Multi-Agent Reinforcement Learning

Yongheng Liang, Hejun Wu, Haitao Wang, Hao Cai

TL;DR

This work tackles credit assignment in multi-agent reinforcement learning under asynchronous decision-making by introducing Virtual Synchrony Proxy (VSP) and Multiplicative Value Decomposition (MVD). VSP virtualizes asynchronous actions to a unified time step, preserving task equilibrium and convergence of VD methods, while MVD leverages multiplicative interactions to capture dependencies across asynchronous decisions. The authors prove theoretical guarantees for VSP and demonstrate that MVD expands the representational capacity beyond additive VD, with ablations showing two-way improvements in convergence speed and interpretability. Empirically, MVD with VSP outperforms state-of-the-art baselines across asynchronous MARL benchmarks (SMAC, Overcooked, POAC), particularly in complex tasks, and provides interpretable insights into the asynchronous credit flow.

Abstract

Credit assignment is a critical problem in multi-agent reinforcement learning (MARL), aiming to identify agents' marginal contributions for optimizing cooperative policies. Current credit assignment methods typically assume synchronous decision-making among agents. However, many real-world scenarios require agents to act asynchronously without waiting for others. This asynchrony introduces conditional dependencies between actions, which pose great challenges to current methods. To address this issue, we propose an asynchronous credit assignment framework, incorporating a Virtual Synchrony Proxy (VSP) mechanism and a Multiplicative Value Decomposition (MVD) algorithm. VSP enables physically asynchronous actions to be virtually synchronized during credit assignment. We theoretically prove that VSP preserves both task equilibrium and algorithm convergence. Furthermore, MVD leverages multiplicative interactions to effectively model dependencies among asynchronous actions, offering theoretical advantages in handling asynchronous tasks. Extensive experiments show that our framework consistently outperforms state-of-the-art MARL methods on challenging tasks while providing improved interpretability for asynchronous cooperation.

Asynchronous Credit Assignment for Multi-Agent Reinforcement Learning

TL;DR

This work tackles credit assignment in multi-agent reinforcement learning under asynchronous decision-making by introducing Virtual Synchrony Proxy (VSP) and Multiplicative Value Decomposition (MVD). VSP virtualizes asynchronous actions to a unified time step, preserving task equilibrium and convergence of VD methods, while MVD leverages multiplicative interactions to capture dependencies across asynchronous decisions. The authors prove theoretical guarantees for VSP and demonstrate that MVD expands the representational capacity beyond additive VD, with ablations showing two-way improvements in convergence speed and interpretability. Empirically, MVD with VSP outperforms state-of-the-art baselines across asynchronous MARL benchmarks (SMAC, Overcooked, POAC), particularly in complex tasks, and provides interpretable insights into the asynchronous credit flow.

Abstract

Credit assignment is a critical problem in multi-agent reinforcement learning (MARL), aiming to identify agents' marginal contributions for optimizing cooperative policies. Current credit assignment methods typically assume synchronous decision-making among agents. However, many real-world scenarios require agents to act asynchronously without waiting for others. This asynchrony introduces conditional dependencies between actions, which pose great challenges to current methods. To address this issue, we propose an asynchronous credit assignment framework, incorporating a Virtual Synchrony Proxy (VSP) mechanism and a Multiplicative Value Decomposition (MVD) algorithm. VSP enables physically asynchronous actions to be virtually synchronized during credit assignment. We theoretically prove that VSP preserves both task equilibrium and algorithm convergence. Furthermore, MVD leverages multiplicative interactions to effectively model dependencies among asynchronous actions, offering theoretical advantages in handling asynchronous tasks. Extensive experiments show that our framework consistently outperforms state-of-the-art MARL methods on challenging tasks while providing improved interpretability for asynchronous cooperation.
Paper Structure (35 sections, 6 theorems, 30 equations, 20 figures, 4 tables, 1 algorithm)

This paper contains 35 sections, 6 theorems, 30 equations, 20 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Preliminaries
  4. Dec-POMDP
  5. Credit Assignment and Value Decomposition
  6. Virtual Synchrony Proxy
  7. MVD
  8. Multiplicative Interaction Form
  9. High-Order Interaction Form
  10. Implementation
  11. Experiments
  12. Benchmark Results
  13. Ablation Studies
  14. Interpretability
  15. Conclusion and Future Work
  16. ...and 20 more sections

Key Result

Theorem 1

Given an asynchronous decision-making task, define $\bm{\pi}_{Dec}^*$ and $\bm{\pi}_{VSP}^*$ as the Markov Perfect Equilibrium (MPE) respectively for modeling as a Dec-POMDP and a Dec-POMDP with VSP. $\mathcal{T}_{Dec}^*$ and $\mathcal{T}_{VSP}^*$ as the VD operator for the same. Assuming that $\mat

Figures (20)

  • Figure 1: Illustration of various asynchronous MARL frameworks. Blue/Yellow circles denote agent $\#x$'s action $a_x^y$ at time step $t_y$. Small white circles denote padding actions. Stars denote action completion. (a) Agents $\#2$ and $\#3$ must wait for agent $\#1$ to finish at $t_1$ before making next decisions. (b) Agent $\#2$ disregards action $a_1^4$ from $t_2$ to $t_5$. (c) Credit at $t_2$ is attributed to the padding actions. (d) Our proposed framework captures the interactions among asynchronous decisions being executed at $t_2$.
  • Figure 2: At $t_2$, $a_1^0$ and $a_3^1$ are executing. (a) Virtual proxies $\#1'$ and $\#3'$ are introduced to re-execute $a_1^0$ and $a_3^1$. (b) The policies of agent $\#1$ and $\#3$ for $a_1^0$ and $a_3^1$ are updated through their proxies.
  • Figure 3: The overall framework of MVD. Left: Mixing network structure. In red are the hypernetworks that generate the weights and biases for mixing network. Middle: The overall MVD architecture. Right: Agent network structure.
  • Figure 4: Performance on two challenging asynchronous scenarios
  • Figure 5: Test win rate % on three scenarios of POAC benchmark.
  • ...and 15 more figures

Theorems & Definitions (17)

  • Definition 1: Dec-POMDP with VSP
  • Theorem 1
  • proof
  • Theorem 2
  • proof
  • Definition 2
  • Definition 3
  • Definition 4
  • Definition 5
  • Lemma 1
  • ...and 7 more