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An Organizationally-Oriented Approach to Enhancing Explainability and Control in Multi-Agent Reinforcement Learning

Julien Soulé, Jean-Paul Jamont, Michel Occello, Louis-Marie Traonouez, Paul Théron

TL;DR

The paper tackles the challenge of explainability and control in multi-agent reinforcement learning by embedding the explicit organizational model $ M O I S E^+$ into the MARL framework via MOISE+MARL. It introduces TEMM, a trajectory-based unsupervised method to infer implicit roles, goals, and obligations from observed behaviors and to quantify organizational fit. Empirical results across four Dec-POMDP environments show that enforcing MOISE^+ constraints improves organizational fit, convergence, and robustness, with clear advantages over AGR+MARL and across multiple MARL algorithms. The approach offers a modular, externally-guided mechanism to shape cooperative behavior, enabling more predictable and auditable multi-agent systems, while outlining practical limitations and directions for dynamic adaptation and automation of organizational specifications.

Abstract

Multi-Agent Reinforcement Learning can lead to the development of collaborative agent behaviors that show similarities with organizational concepts. Pushing forward this perspective, we introduce a novel framework that explicitly incorporates organizational roles and goals from the $\mathcal{M}OISE^+$ model into the MARL process, guiding agents to satisfy corresponding organizational constraints. By structuring training with roles and goals, we aim to enhance both the explainability and control of agent behaviors at the organizational level, whereas much of the literature primarily focuses on individual agents. Additionally, our framework includes a post-training analysis method to infer implicit roles and goals, offering insights into emergent agent behaviors. This framework has been applied across various MARL environments and algorithms, demonstrating coherence between predefined organizational specifications and those inferred from trained agents.

An Organizationally-Oriented Approach to Enhancing Explainability and Control in Multi-Agent Reinforcement Learning

TL;DR

The paper tackles the challenge of explainability and control in multi-agent reinforcement learning by embedding the explicit organizational model into the MARL framework via MOISE+MARL. It introduces TEMM, a trajectory-based unsupervised method to infer implicit roles, goals, and obligations from observed behaviors and to quantify organizational fit. Empirical results across four Dec-POMDP environments show that enforcing MOISE^+ constraints improves organizational fit, convergence, and robustness, with clear advantages over AGR+MARL and across multiple MARL algorithms. The approach offers a modular, externally-guided mechanism to shape cooperative behavior, enabling more predictable and auditable multi-agent systems, while outlining practical limitations and directions for dynamic adaptation and automation of organizational specifications.

Abstract

Multi-Agent Reinforcement Learning can lead to the development of collaborative agent behaviors that show similarities with organizational concepts. Pushing forward this perspective, we introduce a novel framework that explicitly incorporates organizational roles and goals from the model into the MARL process, guiding agents to satisfy corresponding organizational constraints. By structuring training with roles and goals, we aim to enhance both the explainability and control of agent behaviors at the organizational level, whereas much of the literature primarily focuses on individual agents. Additionally, our framework includes a post-training analysis method to infer implicit roles and goals, offering insights into emergent agent behaviors. This framework has been applied across various MARL environments and algorithms, demonstrating coherence between predefined organizational specifications and those inferred from trained agents.

Paper Structure

This paper contains 30 sections, 4 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related works
  3. Evaluating organizational fit
  4. Controlling organizational fit
  5. The MOISE+MARL framework
  6. Markov framework for MARL
  7. The $\mathcal{M}OISE^+$ organizational model
  8. Structural Specifications (SS)
  9. Functional Specifications (FS)
  10. Deontic Specifications (DS)
  11. Linking $\mathcal{M}OISE^+$ with MARL
  12. Resolving the MOISE+MARL problem
  13. Easying constraint guides implementation
  14. The TEMM method
  15. 1) Inferring roles and their inheritance
  16. ...and 15 more sections

Figures (2)

  • Figure 1: A synthetic view of the $\mathcal{M}OISE^+$ model
  • Figure 2: A minimal view of the MOISE+MARL framework: Users first define $\mathcal{M}OISE^+$ specifications, which include roles ($\mathcal{R}$) and missions ($\mathcal{M}$), both associated through $rds$. They then create MOISE+MARL specifications by first defining Constraint guides such as $rag$ and $rrg$ to specify role logic, and $grg$ for goal logic. Next, Linkers are used to connect agents with roles through $ar$ and to link the logic of the constraint guides to the defined $\mathcal{M}OISE^+$ specifications. Once this is set up, roles can be assigned to agents, and the MARL framework updates accordingly during training.