Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning

doi:10.1109/TITS.2024.3378007

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Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning

2024-09-01

Jing Tan *

Ramin Khalili

Holger Karl

DOI：10.1109/TITS.2024.3378007

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Abstract

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The Intelligent Transportation System (ITS) environment is known to be dynamic and distributed, where participants (vehicle users, operators, etc.) have multiple, changing and possibly conflicting objectives. Although Reinforcement Learning (RL) algorithms are commonly applied to optimize ITS applications such as resource management and offloading, most RL algorithms focus on single objectives. In many situations, converting a multi-objective problem into a single-objective one is impossible, intractable or insufficient, making such RL algorithms inapplicable. We propose a multi-objective, multi-agent reinforcement learning (MARL) algorithm with high learning efficiency and low computational requirements, which automatically triggers adaptive few-shot learning in a dynamic, distributed and noisy environment with sparse and delayed reward. We test our algorithm in an ITS environment with edge cloud computing. Empirical results show that the algorithm is quick to adapt to new environments and performs better in all individual and system metrics compared to the state-of-the-art benchmark. Our algorithm also addresses various practical concerns with its modularized and asynchronous online training method. In addition to the cloud simulation, we test our algorithm on a single-board computer and show that it can make inference in 6 milliseconds.

Keywords:

Heuristic algorithms

Computational modeling

Vehicle dynamics

Resource management

Optimization

Inference algorithms

Complexity theory

V2X

distributed systems

reinforcement learning

multi-objective