A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles

TL;DR

This paper compares different exploration methods in deep reinforcement learning for energy management in hybrid electric vehicles, finding that parameter space noise offers the best stability and convergence speed for transfer learning.

Contribution

It introduces a comparison of exploration strategies in transfer learning for DRL-based EMS in HEVs, highlighting the superiority of parameter space noise.

Findings

Parameter space noise improves stability and convergence speed

Action space noise combined with parameter space noise performs poorly

Transfer learning accelerates EMS training in HEVs

Abstract

The deep reinforcement learning-based energy management strategies (EMS) have become a promising solution for hybrid electric vehicles (HEVs). When driving cycles are changed, the neural network will be retrained, which is a time-consuming and laborious task. A more efficient way of choosing EMS is to combine deep reinforcement learning (DRL) with transfer learning, which can transfer knowledge of one domain to the other new domain, making the network of the new domain reach convergence values quickly. Different exploration methods of DRL, including adding action space noise and parameter space noise, are compared against each other in the transfer learning process in this work. Results indicate that the network added parameter space noise is more stable and faster convergent than the others. In conclusion, the best exploration method for transferable EMS is to add noise in the parameter space, while the combination of action space noise and parameter space noise generally performs poorly. Our code is available at https://github.com/BIT-XJY/RL-based-Transferable-EMS.git.