Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning

Abstract: Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server, with each agent solving varied tasks over time. To achieve efficient collaboration, agents should: i) autonomously identify beneficial collaborative relationships in a decentralized manner; and ii) adapt to dynamically changing task observations. In this paper, we propose DeLAMA, a decentralized multi-agent lifelong collaborative learning algorithm with dynamic collaboration graphs. To promote autonomous collaboration relationship learning, we propose a decentralized graph structure learning algorithm, eliminating the need for external priors. To facilitate adaptation to dynamic tasks, we design a memory unit to capture the agents' accumulated learning history and knowledge, while preserving finite storage consumption. To further augment the system's expressive capabilities and computational efficiency, we apply algorithm unrolling, leveraging the advantages of both mathematical optimization and neural networks. This allows the agents to `learn to collaborate' through the supervision of training tasks. Our theoretical analysis verifies that inter-agent collaboration is communication efficient under a small number of communication rounds. The experimental results verify its ability to facilitate the discovery of collaboration strategies and adaptation to dynamic learning scenarios, achieving a 98.80% reduction in MSE and a 188.87% improvement in classification accuracy. We expect our work can serve as a foundational technique to facilitate future works towards an intelligent, decentralized, and dynamic multi-agent system. Code is available at https://github.com/ShuoTang123/DeLAMA.

• The paper introduces DeLAMA, a decentralized algorithm that forms dynamic collaboration graphs and incorporates a memory unit to continually adapt to evolving tasks.
• It employs algorithm unrolling and meta-learning to transform optimization iterations into neural network layers, thereby reducing communication overhead and simplifying hyperparameter tuning.
• Experimental validation shows that DeLAMA outperforms baseline methods by reducing mean squared error and improving classification accuracy in diverse applications.

Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning

This paper presents DeLAMA, a decentralized algorithm for lifelong-adaptive multi-agent collaborative learning. The goal is to enhance collaboration among agents without a central server, enabling them to solve varied tasks over time autonomously. DeLAMA introduces decentralized dynamic collaboration graphs and a memory unit for adapting to dynamic tasks, leveraging both optimization and neural networks. This essay provides a detailed analysis of DeLAMA's architecture, theoretical insights, and experimental validation.

System Architecture

DeLAMA is built upon a multi-agent learning framework where agents operate without a central server. Each agent is responsible for dynamically identifying beneficial collaboration links while adapting its learning process to accommodate evolving tasks.

Figure 1: The illustration of our decentralized and lifelong-adaptive multi-agent collaborative learning system.

Key Components

1. Local Learning: Agents update their learning history and initialize model parameters based on new observations. This step prepares agents for collaborative relational inference.
2. Collaborative Relational Inference: This step involves determining dynamic collaborative relationships using decentralized graph learning. Agents share model parameters and update their communication links without a central server, promoting resilience and efficiency.
3. Lifelong Model Update: Agents continuously adapt their models by integrating past learning experiences. This component uses a memory unit to retain salient information, minimizing storage requirements and facilitating dynamic adaptation.

   Figure 2: The decentralized and lifelong-adaptive multi-agent collaborative learning system.

Algorithm Unrolling

The paper leverages algorithm unrolling to transform optimization iterations into layers of a neural network. This approach enhances DeLAMA's expressive power, facilitating robust learning across different tasks. Specifically, unrolling reduces communication overhead and simplifies hyperparameter tuning through the network's learnable parameters.

Figure 3: The unrolled network structure of the collaboration system with input data at one exact time.

Training Process

DeLAMA employs a meta-learning paradigm that involves two learning phases:

• Learning-to-Learn: Using diverse tasks to train the collaborative strategy, optimizing the network hyperparameters.
• Model Learning and Inference: Agents refine their model parameters through a forward pass in the trained network, enabling versatile task adaptations.

Theoretical Analysis

DeLAMA's theoretical framework provides insights into its optimization properties. Notably:

• Decentralized Graph Learning: The paper establishes convergence properties of the optimized graph structure, ensuring robust collaborative relations among agents.
• Optimal Model Adaptation: It proves a linear convergence rate for model updates, highlighting the efficiency of the parameter adaptation process.

Experimental Validation

DeLAMA's performance is validated across diverse tasks such as regression, image classification, and multi-robot mapping. Key findings demonstrate significant improvements over existing decentralized and federated learning methods.

Figure 4: The simulation result of robot mapping at t=12 based on the CubiCasa5k room Dataset.

Key Results

• Task Performance: DeLAMA achieves a notable reduction in mean squared error (MSE) and improvement in classification accuracy across experiments.
• Collaborative Efficiency: It outperforms baseline methods in learning accurate collaboration graphs, essential for effective multi-agent cooperation.

Conclusion

DeLAMA presents a significant advance in decentralized multi-agent learning by combining mathematical optimization with neural networks. It efficiently balances collaboration and dynamic task adaptation without central oversight, paving the way for more resilient and adaptive multi-agent systems. The framework's theoretical and empirical successes suggest its applicability in real-world collaborative scenarios. Future work could explore further scalability and application to more complex environments.