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Deep RL reward function design for lane-free autonomous driving

Karalakou Athanasia, Troullinos Dimitrios, Chalkiadakis Georgios, Papageorgiou Markos

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Year 2022
Type of Item Conference Full Paper
Bibliographic Citation A. Karalakou, D. Troullinos, G. Chalkiadakis, and M. Papageorgiou, “Deep RL reward function design for lane-free autonomous driving,” in Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection, vol 13616, Lecture Notes in Computer Science, Cham, Switzerland: Springer Nature, 2022, pp. 254–266, doi: 10.1007/978-3-031-18192-4_21.
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In this paper we present an application of Deep Reinforcement Learning to lane-free traffic, where vehicles do not adhere to the notion of lanes, but are rather able to be located at any lateral position within the road boundaries. This constitutes an entirely different problem domain for autonomous driving compared to lane-based traffic, as vehicles consider the actual two dimensional space available, and their decision making needs to adapt to this concept. We also consider that each vehicle wishes to maintain a (different) desired speed, therefore creating many situations where vehicles need to perform overtaking, and react appropriately to the behaviour of others. As such, in this work, we design a Reinforcement Learning agent for the problem at hand, considering different components of reward functions tied to the environment at various levels of information. Finally, we examine the effectiveness of our approach using the Deep Deterministic Policy Gradient algorithm.

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