A Bayesian Approach to Reinforcement Learning of Vision-Based Vehicular Control
Autor: | Zahra Gharaee, Linbo He, Michael Felsberg, Karl Holmquist |
---|---|
Rok vydání: | 2021 |
Předmět: |
FOS: Computer and information sciences
Ground truth Computer Science - Artificial Intelligence business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Bayesian probability Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Image segmentation 010501 environmental sciences Semantics 01 natural sciences Artificial Intelligence (cs.AI) 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Reinforcement learning 020201 artificial intelligence & image processing Segmentation Artificial intelligence Temporal difference learning business 0105 earth and related environmental sciences |
Zdroj: | ICPR |
Popis: | In this paper, we present a state-of-the-art reinforcement learning method for autonomous driving. Our approach employs temporal difference learning in a Bayesian framework to learn vehicle control signals from sensor data. The agent has access to images from a forward facing camera, which are preprocessed to generate semantic segmentation maps. We trained our system using both ground truth and estimated semantic segmentation input. Based on our observations from a large set of experiments, we conclude that training the system on ground truth input data leads to better performance than training the system on estimated input even if estimated input is used for evaluation. The system is trained and evaluated in a realistic simulated urban environment using the CARLA simulator. The simulator also contains a benchmark that allows for comparing to other systems and methods. The required training time of the system is shown to be lower and the performance on the benchmark superior to competing approaches. |
Databáze: | OpenAIRE |
Externí odkaz: |