Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning

Yoann Poupart, Aurélie Beynier, Nicolas Maudet

TL;DR

The paper addresses the interpretability gap in Multi-Agent Deep Reinforcement Learning (MADRL) by advocating direct, post-hoc interpretability methods that extract explanations from trained deep networks without altering architectures. It surveys and contextualizes a range of methods—feature importance, prototypes, latent manipulation, and circuit analysis—and maps them to single-agent, multi-agent, and training-process challenges. Key contributions include a taxonomy for applying direct interpretability in MADRL, a synthesis of methods across agent roles and training stages, and proposed directions for team identification, swarm coordination, and sample efficiency. The work highlights the practical significance of interpretable MADRL systems for safety, accountability, and governance in real-world applications, while also calling for robust evaluation protocols to address limitations such as explanation illusions and causal disentanglement.

Abstract

Multi-Agent Deep Reinforcement Learning (MADRL) was proven efficient in solving complex problems in robotics or games, yet most of the trained models are hard to interpret. While learning intrinsically interpretable models remains a prominent approach, its scalability and flexibility are limited in handling complex tasks or multi-agent dynamics. This paper advocates for direct interpretability, generating post hoc explanations directly from trained models, as a versatile and scalable alternative, offering insights into agents' behaviour, emergent phenomena, and biases without altering models' architectures. We explore modern methods, including relevance backpropagation, knowledge edition, model steering, activation patching, sparse autoencoders and circuit discovery, to highlight their applicability to single-agent, multi-agent, and training process challenges. By addressing MADRL interpretability, we propose directions aiming to advance active topics such as team identification, swarm coordination and sample efficiency.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning

TL;DR

The paper addresses the interpretability gap in Multi-Agent Deep Reinforcement Learning (MADRL) by advocating direct, post-hoc interpretability methods that extract explanations from trained deep networks without altering architectures. It surveys and contextualizes a range of methods—feature importance, prototypes, latent manipulation, and circuit analysis—and maps them to single-agent, multi-agent, and training-process challenges. Key contributions include a taxonomy for applying direct interpretability in MADRL, a synthesis of methods across agent roles and training stages, and proposed directions for team identification, swarm coordination, and sample efficiency. The work highlights the practical significance of interpretable MADRL systems for safety, accountability, and governance in real-world applications, while also calling for robust evaluation protocols to address limitations such as explanation illusions and causal disentanglement.

Abstract

Multi-Agent Deep Reinforcement Learning (MADRL) was proven efficient in solving complex problems in robotics or games, yet most of the trained models are hard to interpret. While learning intrinsically interpretable models remains a prominent approach, its scalability and flexibility are limited in handling complex tasks or multi-agent dynamics. This paper advocates for direct interpretability, generating post hoc explanations directly from trained models, as a versatile and scalable alternative, offering insights into agents' behaviour, emergent phenomena, and biases without altering models' architectures. We explore modern methods, including relevance backpropagation, knowledge edition, model steering, activation patching, sparse autoencoders and circuit discovery, to highlight their applicability to single-agent, multi-agent, and training process challenges. By addressing MADRL interpretability, we propose directions aiming to advance active topics such as team identification, swarm coordination and sample efficiency.

Paper Structure

This paper contains 34 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Background
  3. Multi-Agent Deep Reinforcement Learning
  4. Direct Interpretability of DNNs
  5. Feature importance.
  6. Prototypes:
  7. Latent manipulation:
  8. Circuit analysis:
  9. Advocating Direct Interpretability
  10. Single-Agent Challenges
  11. Biases identification:
  12. Policy distillation,
  13. Decision decomposition:
  14. Policy edition,
  15. Multi-Agent Challenges
  16. ...and 19 more sections

Figures (2)

  • Figure 1: Visual taxonomy of MADRL challenges that could benefit from direct interpretability methods. In green (dots) challenges related to a single agent, in blue (short dashes) to multiple agents and in red (long dashes) to the training process.
  • Figure 2: Schema of a simplified view of MADRL systems. At each time step, the agent $i$ receives the initial observation $o_i$, complemented by potential communications $c_i$ and produces an action $a_i$. The agent learns throughout training by the means of gradients $\nabla_i$.