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Reinforcement Networks: novel framework for collaborative Multi-Agent Reinforcement Learning tasks

Maksim Kryzhanovskiy, Svetlana Glazyrina, Roman Ischenko, Konstantin Vorontsov

TL;DR

Reinforcement Networks present a DAG-based framework for collaborative MARL that unifies hierarchical, modular, and graph-structured agent coordination, connected to the LevelEnv abstraction to support reproducible training and evaluation. By embedding agents as vertices in a directed acyclic graph and allowing upstream (messages/rewards) and downstream (actions/instructions) information flows, the method enables flexible credit assignment and scalable coordination beyond rigid trees or fully centralized schemes. Empirical studies on MPE Simple Spread and VMAS Balance show that reinforced hierarchical configurations with bridging skip-connections and learned communication improve stability and sample efficiency relative to baselines like IPPO and 3PPO, while still accommodating multi-time-scale operation. The framework thus offers a principled path toward designing complex, structured multi-agent systems and points to future work on topology optimization, proxy-reward/communication training, and applications to LLM-based agents.

Abstract

Modern AI systems often comprise multiple learnable components that can be naturally organized as graphs. A central challenge is the end-to-end training of such systems without restrictive architectural or training assumptions. Such tasks fit the theory and approaches of the collaborative Multi-Agent Reinforcement Learning (MARL) field. We introduce Reinforcement Networks, a general framework for MARL that organizes agents as vertices in a directed acyclic graph (DAG). This structure extends hierarchical RL to arbitrary DAGs, enabling flexible credit assignment and scalable coordination while avoiding strict topologies, fully centralized training, and other limitations of current approaches. We formalize training and inference methods for the Reinforcement Networks framework and connect it to the LevelEnv concept to support reproducible construction, training, and evaluation. We demonstrate the effectiveness of our approach on several collaborative MARL setups by developing several Reinforcement Networks models that achieve improved performance over standard MARL baselines. Beyond empirical gains, Reinforcement Networks unify hierarchical, modular, and graph-structured views of MARL, opening a principled path toward designing and training complex multi-agent systems. We conclude with theoretical and practical directions - richer graph morphologies, compositional curricula, and graph-aware exploration. That positions Reinforcement Networks as a foundation for a new line of research in scalable, structured MARL.

Reinforcement Networks: novel framework for collaborative Multi-Agent Reinforcement Learning tasks

TL;DR

Reinforcement Networks present a DAG-based framework for collaborative MARL that unifies hierarchical, modular, and graph-structured agent coordination, connected to the LevelEnv abstraction to support reproducible training and evaluation. By embedding agents as vertices in a directed acyclic graph and allowing upstream (messages/rewards) and downstream (actions/instructions) information flows, the method enables flexible credit assignment and scalable coordination beyond rigid trees or fully centralized schemes. Empirical studies on MPE Simple Spread and VMAS Balance show that reinforced hierarchical configurations with bridging skip-connections and learned communication improve stability and sample efficiency relative to baselines like IPPO and 3PPO, while still accommodating multi-time-scale operation. The framework thus offers a principled path toward designing complex, structured multi-agent systems and points to future work on topology optimization, proxy-reward/communication training, and applications to LLM-based agents.

Abstract

Modern AI systems often comprise multiple learnable components that can be naturally organized as graphs. A central challenge is the end-to-end training of such systems without restrictive architectural or training assumptions. Such tasks fit the theory and approaches of the collaborative Multi-Agent Reinforcement Learning (MARL) field. We introduce Reinforcement Networks, a general framework for MARL that organizes agents as vertices in a directed acyclic graph (DAG). This structure extends hierarchical RL to arbitrary DAGs, enabling flexible credit assignment and scalable coordination while avoiding strict topologies, fully centralized training, and other limitations of current approaches. We formalize training and inference methods for the Reinforcement Networks framework and connect it to the LevelEnv concept to support reproducible construction, training, and evaluation. We demonstrate the effectiveness of our approach on several collaborative MARL setups by developing several Reinforcement Networks models that achieve improved performance over standard MARL baselines. Beyond empirical gains, Reinforcement Networks unify hierarchical, modular, and graph-structured views of MARL, opening a principled path toward designing and training complex multi-agent systems. We conclude with theoretical and practical directions - richer graph morphologies, compositional curricula, and graph-aware exploration. That positions Reinforcement Networks as a foundation for a new line of research in scalable, structured MARL.
Paper Structure (44 sections, 14 equations, 5 figures, 1 algorithm)

This paper contains 44 sections, 14 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. From learnable systems to collaborative MARL.
  3. Leveled architectures and the LevelEnv abstraction.
  4. Why DAGs for multi-agent systems?
  5. Our research.
  6. Related Works
  7. Multi-Agent Reinforcement Learning
  8. Hierarchical Reinforcement Learning
  9. Methodology
  10. Fundamentals
  11. Multi-Agent Reinforcement Learning
  12. Proposed model
  13. Reinforcement Networks
  14. Upstream and downstream inference
  15. Sharing experience.
  16. ...and 29 more sections

Figures (5)

  • Figure 1: Considering agent $\omega_2$ colored yellow, its subordinate agents $V_2^-$ are marked blue and the superior agents are marked red. Edges of the DAG represent agents' couples where direct communication is present. Red arrows depict interaction between motors and the real environment.
  • Figure 2: Example of inference in the system. Left: downstream inference. Right: upstream inference.
  • Figure 3: Information flow from the perspective of agent $w_i$. Rounded rectangles indicate $V_i^+$ (red) and $V_i^-$ (blue). Actions propagate downstream (red), while messages and rewards flow upstream (blue). One timestep corresponds to a counterclockwise cycle starting at the upper-right corner.
  • Figure 4: Transformation of a directed acyclic graph (DAG) into a layered digraph.
  • Figure 5: Mean episode reward in the MPE Simple Spread (a) and VMAS Balance (b) environments. Results are averaged over 5 random seeds. Shaded regions denote 95% confidence intervals.