Decentralized Neural Network Policies

Updated 8 December 2025

Decentralized neural-network policies are mappings that let each agent select actions based solely on local observations, enhancing privacy and robustness.
They utilize diverse architectures such as MLPs, CNNs, and GNNs to achieve scalability and fault tolerance in multi-agent environments.
Training approaches include centralized training with decentralized execution, distributed optimization, and behavioral cloning to maintain system stability.

A decentralized neural-network policy is a mapping—usually parameterized by a neural network or set of neural networks—that enables each agent in a multi-agent system to select its own action using only local information available to it, rather than relying on the global system state or the actions of other agents. The defining attributes of such policies are that (i) the execution is decentralized (no central controller), (ii) communication is absent or strictly local, and (iii) neural networks provide the policy parametrization. This paradigm addresses the scalability, robustness, and privacy requirements of large-scale or safety-critical cyber-physical and robotic systems, as well as cooperative or competitive multi-agent reinforcement learning (MARL) settings.

1. Fundamental Representations and Motivations

The primary motivation for decentralized neural-network policies arises from physical and cyber-physical domains—robotics, smart grids, wireless networks—where system-wide sensing and coordination are impractical due to bandwidth, privacy, or fault-tolerance constraints. In Dec-POMDP formulations, each agent $i$ operates with a local observation $o_i$ and produces an action $a_i$ according to a policy $\pi_i(a_i|o_i; \theta_i)$ , where $\theta_i$ designates the neural-network parameters. The system may include $N$ agents, and in the absence of communication, $o_i$ depends solely on local or neighborhood state variables. Modern approaches often refine this structure by allowing message-passing protocols or encoding observations over a communication graph; however, the essential property is that decision-making and inference require only partial or local information at test time (Dobbe et al., 2017, Blumenkamp et al., 2021, Zakwan et al., 26 Mar 2024).

Decentralized parametrization is motivated by requirements for resilience (as a single agent or channel failure does not collapse system performance), scalability (each agent’s neural network is compact relative to a joint policy over all agents), and, in many cases, robustness to local disturbances (Guo et al., 2023).

2. Neural-Network Architectures and Variants

A diverse spectrum of neural architectures has been employed. Standard designs include multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and, in systems with graph-structured communication or dynamics, graph neural networks (GNNs) and spatiotemporal GNNs (STGNNs).

MLP-based Policies: Each agent maintains its own MLP policy (no parameter sharing), as in distributed deterministic actor-critic frameworks (Bolliger et al., 9 Mar 2025, Dai et al., 30 May 2025).
Parameter Sharing: Homogeneous agents may share network weights to improve training sample efficiency and generalization (Cao et al., 2021, Blumenkamp et al., 2021).
Recurrent Architectures: For problems involving partial observability or temporally extended actions (macro-actions/options), agents maintain local RNNs (e.g., LSTM) to encode observation history, as in MacDec-MADDRQN (Xiao et al., 2019).
Graph Neural Networks: Agents process local observations and messages using permutation-equivariant GNNs, which enable natural scalability to large teams and arbitrary communication topologies (Chen et al., 2023, Blumenkamp et al., 2021, Chen et al., 4 Apr 2024).
Modular or Branch Decomposition: In motor skill control (DEMOS), a robot’s kinematics is decomposed into physically meaningful modules. Each module governs a subset of actuators and is assigned a dedicated subnetwork (Guo et al., 2023).

A representative example is the decentralized attention-based transformer DAN, where all agents share model parameters but process distinct local embeddings, using self- and cross-attention to encode their perspective of the global task (Cao et al., 2021).

3. Training Algorithms and Decentralized Optimization

A central distinction is between the training and execution paradigms. While execution must be fully decentralized, training may be fully centralized, partially centralized, or itself decentralized. Key frameworks include:

Centralized Training, Decentralized Execution (CTDE): Agents have access to joint state/action/reward signals and possibly a centralized critic at training time but are deployed using only local policies (Xiao et al., 2019, Blumenkamp et al., 2021, Guo et al., 2023).
Decentralized (or Networked) Training: Agents train using only their own data (local observations, local rewards) and communicate parameters or critic estimates with neighbors on a communication graph, using consensus averaging or soft parameter blending (Bolliger et al., 9 Mar 2025, Dai et al., 30 May 2025, Li et al., 2021).
Behavioral Cloning: Policies may be trained via supervised imitation learning from centralized or expert demonstrations (Chen et al., 2023).
Distributed Optimization: Algorithms leverage consensus-based gradient steps (e.g., distributed TD for critics, decentralized policy-gradient for actors, ADMM for consensus in quadratic-program constrained models) to jointly optimize policies while respecting locality (Pereira et al., 2022, Dai et al., 30 May 2025).

Training objectives typically include policy-gradient loss (reinforcement learning), auxiliary regularization for explicit decentralization (e.g., suppressing cross-module outputs (Guo et al., 2023)), or imitation loss (mean squared error from expert behavior).

4. Mathematical Formulation and Theoretical Guarantees

The policy learning problem is frequently formalized as follows. Given a global system described by a Markov process $(S, A_1 \times \dots \times A_N, P, R, \gamma)$ and a local observation function $O_i: S \to \mathcal{O}_i$ , each agent seeks a policy $\pi_i(a_i|o_i;\theta_i)$ that (either alone or in concert with other local policies $\pi_{-i}$ ) maximizes expected cumulative reward.

In the information-theoretic approach, optimal decentralized policies are characterized using the rate-distortion tradeoff, where the minimum achievable distortion is a function of mutual information between local observations and optimal actions. This provides fundamental lower bounds on performance and a systematic way to identify which additional local variables are most valuable to sense or communicate (Dobbe et al., 2017).

Recent distributed actor-critic algorithms (e.g., distributed neural policy gradient) provide convergence proofs under mixing protocols and local projection constraints (Dai et al., 30 May 2025), while graphon-based transferability guarantees enable scalability from small to large networks in systems parameterized by GNNs (Chen et al., 4 Apr 2024). For modular decomposition, post-training pruning strategies quantify and minimize cross-module coupling, yielding both interpretability and robustness (Guo et al., 2023).

5. Practical Implementations and Representative Application Domains

Empirical validation spans a range of domains:

Swarm and Multi-Robot Systems: Fully decentralized GNN-based policies have been deployed in real-world multi-robot formations, passing through narrow passages relying on only peer-to-peer communication, with ROS2 middleware for message management (Blumenkamp et al., 2021). Decentralized macro-action policies demonstrated successful transfer from simulation to warehouse robot teams using macro-actions for tool delivery (Xiao et al., 2019).
Networked Control and Cyber-Physical Systems: Distributed neural policies have been applied to the path planning of multiple mobile robots, grid power-flow optimization, and consensus of Kuramoto oscillator networks, with careful architectural design ensuring stability and convergence (Zakwan et al., 26 Mar 2024, Dai et al., 30 May 2025, Dobbe et al., 2017).
Sensor Networks and Communication: Decentralized GNN-based policies for estimation and sampling on wireless networks demonstrate permutation-equivariance, transferability to large networks, and resilience to nonstationarity (Chen et al., 4 Apr 2024).
Swarm Coordination and Flocking: Spatiotemporal GNNs enable decentralized emulation of centralized flocking controllers with only local history and communication, tested on simulations and real Crazyflie drones (Chen et al., 2023).

6. Robustness, Scalability, and Limitations

The decentralized neural-network paradigm confers several crucial properties:

Robustness: By limiting the influence of each agent’s observation or submodule to its own action, the system is less sensitive to local disturbances, sensor failures, or communication dropouts (Guo et al., 2023, Blumenkamp et al., 2021).
Scalability: Per-agent computational overhead grows with the local neighborhood size, not the total number of agents. Parameter-sharing (when possible) and permutation-equivariant architectures further enhance scalability (Cao et al., 2021, Chen et al., 4 Apr 2024).
Stability Guarantees: Port-Hamiltonian neural controllers ensure finite $\mathcal{L}_2$ -gain and strict output passivity for any set of neural parameters, enabling unconstrained gradient-based training without post-hoc verification (Zakwan et al., 26 Mar 2024).

Limitations include residual suboptimality in highly non-local tasks (when critical information is unavailable locally), potential training–execution mismatches in partial observability, and higher communication or memory requirements when delayed or multi-hop neighbor information must be aggregated (Xiao et al., 2019, Chen et al., 2023). Some methods require centralized global information during training, which may be inapplicable in privacy-constrained settings.

7. Explainability and Interpretability

Decentralized neural-network policies are fundamentally opaque due to the high-dimensional and nonlinear structure of neural network parametrizations. Specialized methods abstract trajectories into partial orders (Hasse diagrams) over task completions, supporting user queries ("When?", "Why Not?", "What?") about agent behavior in multi-agent reinforcement learning settings. These abstractions are generated automatically from rollouts, supporting succinct and human-interpretable explanations and providing statistically significant improvements in user understanding and satisfaction over per-agent baselines (Boggess et al., 13 Nov 2025).

Decentralized neural-network policies constitute a vibrant, rapidly advancing research direction, with demonstrated scalability, robustness, and adaptability across robotics, cyber-physical systems, communications, and MARL. Research continues to address remaining challenges in optimality, large-scale communication, stability guarantees for general neural architectures, and explainability.