The main purpose of this project was to take a look at reinforcement learning with AWS DeepRacer by providing good policies to use when training reinforcement learning models. Another goal was to analyse model performance and how to tune the performance of a model to achieve a better model. AWS DeepRacer is a 1/18th scale autonomous vehicle which is taught to drive by itself with reinforcement learning along various tracks. Currently DeepRacers are trained for three types of objectives: time trials, object avoidance and head-to-head racing. In this project DeepRacer was trained for time trials and object avoidance. DeepRacer was trained to drive within a simulation created by AWS RoboMaker, and its neural network was updated within AWS SageMaker. DeepRacer could either be driven in simulation or on a physical track. In this project, estimates were created for a required amount of training time for a model. In addition, estimates for the initial training time for a model were created. Moreover, the thesis discusses how significantly agent parameters affect model performance; which approaches work the best in reward functions; how changing hyperparameters affects the model and its performance; how to evaluate model performance from log files; and how to improve the quality of training and model performance by doing log analysis. The results can be used as general guidelines for model training and improvement in reinforcement learning with AWS DeepRacer. Following the policies recommended in the thesis, better and more stable models can be achieved.


    Access

    Download


    Export, share and cite



    Title :

    Reinforcement learning with AWS DeepRacer


    Contributors:

    Publication date :

    2021-01-01


    Type of media :

    Miscellaneous


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Traffic control with reinforcement learning

    GANTI RAGHU KIRAN / SRIVATSA MUDHAKAR / RAO VENKATESH ASHOK RAO et al. | European Patent Office | 2022

    Free access

    Malthusian Reinforcement Learning

    Leibo, JZ / Perolat, J / Hughes, E et al. | BASE | 2019

    Free access

    Minimax Reinforcement Learning

    Chakravorty, Suman / Hyland, David | AIAA | 2003


    TRAFFIC CONTROL WITH REINFORCEMENT LEARNING

    GANTI RAGHU KIRAN / SRIVASTA MUDHAKAR / RAO VENKATESH ASHOK RAO et al. | European Patent Office | 2020

    Free access

    Deep Reinforcement Learning

    Huang, Xiaowei / Jin, Gaojie / Ruan, Wenjie | Springer Verlag | 2012