A Privacy-preserving Distributed Training Framework for Cooperative Multi-agent Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2109.14998v1
• Date: Thu, 30 Sep 2021 10:53:34 GMT
• Title: A Privacy-preserving Distributed Training Framework for Cooperative Multi-agent Deep Reinforcement Learning
• Authors: Yimin Shi
• Abstract summary: We propose a new Deep Neural Network (DNN) architecture with both global NN and local NN, and a distributed training framework. We allow the global weights to be updated by all the collaborator agents while the local weights are only updated by the agent they belong to. Experiments show that the framework can efficiently help agents in the same or similar environments to collaborate in their training process and gain a higher convergence rate and better performance.
• Score: 1.14219428942199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Reinforcement Learning (DRL) sometimes needs a large amount of data to converge in the training procedure and in some cases, each action of the agent may produce regret. This barrier naturally motivates different data sets or environment owners to cooperate to share their knowledge and train their agents more efficiently. However, it raises privacy concerns if we directly merge the raw data from different owners. To solve this problem, we proposed a new Deep Neural Network (DNN) architecture with both global NN and local NN, and a distributed training framework. We allow the global weights to be updated by all the collaborator agents while the local weights are only updated by the agent they belong to. In this way, we hope the global weighs can share the common knowledge among these collaborators while the local NN can keep the specialized properties and ensure the agent to be compatible with its specific environment. Experiments show that the framework can efficiently help agents in the same or similar environments to collaborate in their training process and gain a higher convergence rate and better performance.

Related papers

• Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning [57.652899266553035]
  Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server. We propose DeLAMA, a decentralized multi-agent lifelong collaborative learning algorithm with dynamic collaboration graphs.
  arXiv  Detail & Related papers  (2024-03-11T09:21:11Z)
• Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning with a Use-Case in Resource Allocation in Communication Networks [11.182443036683225]
  Distributed learning and adaptation have received significant interest and found wide-ranging applications in machine learning signal processing. This paper specifically focuses on a scenario where agents collaborate towards a common task. Agents, acting as transmitters, collaboratively train their individual policies to maximize a global reward.
  arXiv  Detail & Related papers  (2023-11-08T11:12:27Z)
• Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning [19.504842607744457]
  In cooperative multi-agent reinforcement learning, a team of agents must jointly optimize the team's long-term rewards to learn a designated task. Privacy-Engineered Value Decomposition Networks (PE-VDN) models multi-agent coordination while safeguarding the confidentiality of the agents' environment interaction data. We implement PE-VDN in StarCraft Multi-Agent Competition (SMAC) and show that it achieves 80% of Vanilla VDN's win rate.
  arXiv  Detail & Related papers  (2023-09-13T02:50:57Z)
• Transferable Multi-Agent Reinforcement Learning with Dynamic Participating Agents [19.52531351740528]
  We propose a network architecture with a few-shot learning algorithm that allows the number of agents to vary during centralized training. Our experiments show that using the proposed network architecture and algorithm, model adaptation when new agents join can be 100+ times faster than the baseline.
  arXiv  Detail & Related papers  (2022-08-04T03:16:42Z)
• Group-Agent Reinforcement Learning [12.915860504511523]
  It can largely benefit the reinforcement learning process of each agent if multiple geographically distributed agents perform their separate RL tasks cooperatively. We propose a distributed RL framework called DDAL (Decentralised Distributed Asynchronous Learning) designed for group-agent reinforcement learning (GARL)
  arXiv  Detail & Related papers  (2022-02-10T16:40:59Z)
• Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning [52.7873574425376]
  Cooperative multi-agent reinforcement learning (MARL) faces significant scalability issues due to state and action spaces that are exponentially large in the number of agents. We propose a novel, value-based multi-agent algorithm called LOMAQ, which incorporates local rewards in the Training Decentralized Execution paradigm.
  arXiv  Detail & Related papers  (2021-09-22T10:08:15Z)
• Learning Connectivity for Data Distribution in Robot Teams [96.39864514115136]
  We propose a task-agnostic, decentralized, low-latency method for data distribution in ad-hoc networks using Graph Neural Networks (GNN) Our approach enables multi-agent algorithms based on global state information to function by ensuring it is available at each robot. We train the distributed GNN communication policies via reinforcement learning using the average Age of Information as the reward function and show that it improves training stability compared to task-specific reward functions.
  arXiv  Detail & Related papers  (2021-03-08T21:48:55Z)
• Cooperative Heterogeneous Deep Reinforcement Learning [47.97582814287474]
  We present a Cooperative Heterogeneous Deep Reinforcement Learning framework that can learn a policy by integrating the advantages of heterogeneous agents. Global agents are off-policy agents that can utilize experiences from the other agents. Local agents are either on-policy agents or population-based evolutionary (EAs) agents that can explore the local area effectively.
  arXiv  Detail & Related papers  (2020-11-02T07:39:09Z)
• Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification [69.29602103582782]
  Deep learning has been successful for many computer vision tasks due to the availability of shared and centralised large-scale training data. However, increasing awareness of privacy concerns poses new challenges to deep learning, especially for person re-identification (Re-ID) We propose a novel paradigm called Federated Person Re-Identification (FedReID) to construct a generalisable global model (a central server) by simultaneously learning with multiple privacy-preserved local models (local clients) This client-server collaborative learning process is iteratively performed under privacy control, enabling FedReID to realise decentralised learning without sharing distributed data nor collecting any
  arXiv  Detail & Related papers  (2020-06-07T13:32:33Z)
• Brainstorming Generative Adversarial Networks (BGANs): Towards Multi-Agent Generative Models with Distributed Private Datasets [70.62568022925971]
  generative adversarial networks (GANs) must be fed by large datasets that adequately represent the data space. In many scenarios, the available datasets may be limited and distributed across multiple agents, each of which is seeking to learn the distribution of the data on its own. In this paper, a novel brainstorming GAN (BGAN) architecture is proposed using which multiple agents can generate real-like data samples while operating in a fully distributed manner.
  arXiv  Detail & Related papers  (2020-02-02T02:58:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.