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Communication-Efficient Soft Actor-Critic Policy Collaboration via Regulated Segment Mixture

Xiaoxue Yu, Rongpeng Li, Chengchao Liang, Zhifeng Zhao

TL;DR

The paper tackles the practicality of centralized or heavily centralized MARL training in dynamic environments by proposing a fully distributed, communication-efficient framework that fuses Decentralized Federated Learning with Maximum Entropy reinforcement learning. It introduces RSM-MASAC, which leverages segmented policy aggregation and a theory-guided mix metric to reconstruct referential policies from neighbors and mix parameters without sacrificing policy improvement. A novel mixed-performance bound under MERL and a Fisher Information Matrix-based constraint guide the selective use of neighbor policies, ensuring soft policy improvement during the communication-assisted phase. Extensive traffic-control experiments demonstrate that RSM-MASAC approaches the performance of centralized counterparts while significantly reducing communication overhead and preserving learning stability. This work advances practical distributed MARL for IoV, IoT, and UAV applications by delivering a scalable, theoretically grounded method for policy collaboration under limited communication.

Abstract

Multi-Agent Reinforcement Learning (MARL) has emerged as a foundational approach for addressing diverse, intelligent control tasks in various scenarios like the Internet of Vehicles, Internet of Things, and Unmanned Aerial Vehicles. However, the widely assumed existence of a central node for centralized, federated learning-assisted MARL might be impractical in highly dynamic environments. This can lead to excessive communication overhead, potentially overwhelming the system. To address these challenges, we design a novel communication-efficient, fully distributed algorithm for collaborative MARL under the frameworks of Soft Actor-Critic (SAC) and Decentralized Federated Learning (DFL), named RSM-MASAC. In particular, RSM-MASAC enhances multi-agent collaboration and prioritizes higher communication efficiency in dynamic systems by incorporating the concept of segmented aggregation in DFL and augmenting multiple model replicas from received neighboring policy segments, which are subsequently employed as reconstructed referential policies for mixing. Distinctively diverging from traditional RL approaches, RSM-MASAC introduces new bounds under the framework of Maximum Entropy Reinforcement Learning (MERL). Correspondingly, it adopts a theory-guided mixture metric to regulate the selection of contributive referential policies, thus guaranteeing soft policy improvement during the communication-assisted mixing phase. Finally, the extensive simulations in mixed-autonomy traffic control scenarios verify the effectiveness and superiority of our algorithm.

Communication-Efficient Soft Actor-Critic Policy Collaboration via Regulated Segment Mixture

TL;DR

The paper tackles the practicality of centralized or heavily centralized MARL training in dynamic environments by proposing a fully distributed, communication-efficient framework that fuses Decentralized Federated Learning with Maximum Entropy reinforcement learning. It introduces RSM-MASAC, which leverages segmented policy aggregation and a theory-guided mix metric to reconstruct referential policies from neighbors and mix parameters without sacrificing policy improvement. A novel mixed-performance bound under MERL and a Fisher Information Matrix-based constraint guide the selective use of neighbor policies, ensuring soft policy improvement during the communication-assisted phase. Extensive traffic-control experiments demonstrate that RSM-MASAC approaches the performance of centralized counterparts while significantly reducing communication overhead and preserving learning stability. This work advances practical distributed MARL for IoV, IoT, and UAV applications by delivering a scalable, theoretically grounded method for policy collaboration under limited communication.

Abstract

Multi-Agent Reinforcement Learning (MARL) has emerged as a foundational approach for addressing diverse, intelligent control tasks in various scenarios like the Internet of Vehicles, Internet of Things, and Unmanned Aerial Vehicles. However, the widely assumed existence of a central node for centralized, federated learning-assisted MARL might be impractical in highly dynamic environments. This can lead to excessive communication overhead, potentially overwhelming the system. To address these challenges, we design a novel communication-efficient, fully distributed algorithm for collaborative MARL under the frameworks of Soft Actor-Critic (SAC) and Decentralized Federated Learning (DFL), named RSM-MASAC. In particular, RSM-MASAC enhances multi-agent collaboration and prioritizes higher communication efficiency in dynamic systems by incorporating the concept of segmented aggregation in DFL and augmenting multiple model replicas from received neighboring policy segments, which are subsequently employed as reconstructed referential policies for mixing. Distinctively diverging from traditional RL approaches, RSM-MASAC introduces new bounds under the framework of Maximum Entropy Reinforcement Learning (MERL). Correspondingly, it adopts a theory-guided mixture metric to regulate the selection of contributive referential policies, thus guaranteeing soft policy improvement during the communication-assisted mixing phase. Finally, the extensive simulations in mixed-autonomy traffic control scenarios verify the effectiveness and superiority of our algorithm.
Paper Structure (26 sections, 10 theorems, 40 equations, 14 figures, 5 tables, 2 algorithms)

This paper contains 26 sections, 10 theorems, 40 equations, 14 figures, 5 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Statement and Motivation
  3. Contribution
  4. Related Works
  5. Paper Organization
  6. Preliminary
  7. System Model and Problem Formulation
  8. System Model
  9. Local Learning Phase
  10. Communication-Assisted Mixing Phase
  11. Problem Formulation
  12. Mixed Performance Improvement Bound Theorem of FRL Communication under MERL
  13. MASAC with Regulated Segment Mixture
  14. Algorithm Design
  15. Segment Request & Response
  16. ...and 11 more sections

Key Result

Theorem 1

(Mixed Policy Improvement Bound) For any policy $\pi$ and $\tilde{\pi}$ adhering to eq:update rule, the improvement in policy performance after mixing can be measured by: where $\varepsilon \!:=\! \max_{s}\vert \mathop{\mathbb{E}}_{a\sim \tilde{\pi}}[A_\pi(s, a)\!+\!\alpha H(\tilde{\pi}(\cdot\vert s))]\vert$ represents the maximum advantage of $\tilde{\pi}$ relative to $\pi$, and $\mathrm{D}_{\ma

Figures (14)

  • Figure 1: Illustration of Single-agent IRL and Multi-agent Distributed Cooperative RL.
  • Figure 2: The illustration of RSM-MASAC implementation.
  • Figure 3: The two scenarios for simulations on Flow. The red vehicles are DRL-driven CAVs, while the blue vehicles are the HDVs observed by the DRL-driven CAVs, and the white vehicles are the HDVs that are not observed in the state space.
  • Figure 4: The runtime of estimating mixture metric under different sample and parameter sizes.
  • Figure 5: Performance of IRL without communication.
  • ...and 9 more figures

Theorems & Definitions (19)

  • Theorem 1
  • Corollary 1
  • Theorem 2
  • proof
  • Lemma 1
  • proof
  • Lemma 2
  • proof
  • Lemma 3
  • proof
  • ...and 9 more