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

arXiv: https://arxiv.org/abs/2202.11514 · HTML: https://mlsanity.com/paper/2202.11514/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [Parameter Space Noise for Exploration](https://mlsanity.com/paper/1706.01905/) (2017)
- [GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms](https://mlsanity.com/paper/1802.05054/) (2018)
- [Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle](https://mlsanity.com/paper/2007.08690/) (2020)
- [Data-Driven Transferred Energy Management Strategy for Hybrid Electric Vehicles via Deep Reinforcement Learning](https://mlsanity.com/paper/2009.03289/) (2020)

## Built on by (most cited first, 1 of 1)

- [RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution Networks](https://mlsanity.com/paper/2408.03685/) (2024)

## Abstract (arXiv)

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.