| Autor: |
Kerbel, Lindsey, Ayalew, Beshah, Ivanco, Andrej, Loiselle, Keith |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
2022 American Control Conference |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.23919/ACC53348.2022.9867557 |
| Popis: |
With the growing need to reduce energy consumption and greenhouse gas emissions, Eco-driving strategies provide a significant opportunity for additional fuel savings on top of other technological solutions being pursued in the transportation sector. In this paper, a model-free deep reinforcement learning (RL) control agent is proposed for active Eco-driving assistance that trades-off fuel consumption against other driver-accommodation objectives, and learns optimal traction torque and transmission shifting policies from experience. The training scheme for the proposed RL agent uses an off-policy actor-critic architecture that iteratively does policy evaluation with a multi-step return and policy improvement with the maximum posteriori policy optimization algorithm for hybrid action spaces. The proposed Eco-driving RL agent is implemented on a commercial vehicle in car following traffic. It shows superior performance in minimizing fuel consumption compared to a baseline controller that has full knowledge of fuel-efficiency tables. |
| Databáze: |
arXiv |
| Externí odkaz: |
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