δείτε την πρωτότυπη σελίδα τεκμηρίου στον ιστότοπο του αποθετηρίου του φορέα για περισσότερες πληροφορίες και για να δείτε όλα τα ψηφιακά αρχεία του τεκμηρίου*
Deep RL reward function design for lane-free autonomous driving
(EN)
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.
(EN)
Deep reinforcement learning
(EN)
Lane-free traffic
(EN)
Autonomous driving
(EN)
20th International Conference on Practical Applications of Agents and Multi-Agent Systems
(EL)
*Η εύρυθμη και αδιάλειπτη λειτουργία των διαδικτυακών διευθύνσεων των συλλογών (ψηφιακό αρχείο, καρτέλα τεκμηρίου στο αποθετήριο) είναι αποκλειστική ευθύνη των αντίστοιχων Φορέων περιεχομένου.
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