Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning

TL;DR

This paper introduces a Blockchain-assisted framework for Multi-Agent Deep Reinforcement Learning that leverages expert demonstrations and model sharing to improve learning efficiency and robustness against malicious models.

Contribution

It proposes a novel Blockchain-based demonstration cloning framework that enables secure model sharing and expert guidance in MDRL, addressing existing challenges in sample efficiency and security.

Findings

Outperforms existing methods in learning speed

Demonstrates robustness against malicious models

Enables traceable and autonomous model sharing

Abstract

Multi-Agent Deep Reinforcement Learning (MDRL) is a promising research area in which agents learn complex behaviors in cooperative or competitive environments. However, MDRL comes with several challenges that hinder its usability, including sample efficiency, curse of dimensionality, and environment exploration. Recent works proposing Federated Reinforcement Learning (FRL) to tackle these issues suffer from problems related to model restrictions and maliciousness. Other proposals using reward shaping require considerable engineering and could lead to local optima. In this paper, we propose a novel Blockchain-assisted Multi-Expert Demonstration Cloning (MEDC) framework for MDRL. The proposed method utilizes expert demonstrations in guiding the learning of new MDRL agents, by suggesting exploration actions in the environment. A model sharing framework on Blockchain is designed to allow users to share their trained models, which can be allocated as expert models to requesting users to aid in training MDRL systems. A Consortium Blockchain is adopted to enable traceable and autonomous execution without the need for a single trusted entity. Smart Contracts are designed to manage users and models allocation, which are shared using IPFS. The proposed framework is tested on several applications, and is benchmarked against existing methods in FRL, Reward Shaping, and Imitation Learning-assisted RL. The results show the outperformance of the proposed framework in terms of learning speed and resiliency to faulty and malicious models.