Multi-agent Robust and Optimal Policy Learning for Data Harvesting

TL;DR

This paper presents a reinforcement learning approach using PPO with Lagrangian Penalty to optimize and robustly control multiple agents for efficient data collection from scattered sensor nodes in a 2D environment.

Contribution

It introduces a novel multi-agent policy learning framework that enhances robustness through regularization and applies PPO with LP for data harvesting tasks.

Findings

The proposed method achieves high efficiency in data collection.

The approach demonstrates robustness against environmental variations.

Simulations validate the effectiveness of the learned policies.

Abstract

We consider the problem of using multiple agents to harvest data from a collection of sensor nodes (targets) scattered across a two-dimensional environment. These targets transmit their data to the agents that move in the space above them, and our goal is for the agents to collect data from the targets as efficiently as possible while moving to their final destinations. The agents are assumed to have a continuous control action, and we leverage reinforcement learning, specifically Proximal Policy Optimization (PPO) with Lagrangian Penalty (LP), to identify highly effective solutions. Additionally, we enhance the controller's robustness by incorporating regularization at each state to smooth the learned policy. We conduct a series of simulations to demonstrate our approach and validate its performance and robustness.