This repo contains the source code for the paper entitled, "High-speed Autonomous Racing using Trajectory-aided Deep Reinforcement Learning"
We present a reward signal that incorporates an optimal trajectory to train deep reinforcement learning agents for high-speed autonomous racing.
Training agents with our reward signal results in significatly improved training performance. The most noteable performance difference is at high-speeds where previous rewards failed.
The improved training results in higher average progrresses at high speeds.
The results in the paper are generated through a two step process of:
- Train and test the agents
- Process and plot the data
For every test:
- Run calculate_statistics
- Run calculate_averages
- Aim: Understand how performance changes with different speeds.
- Config files: CthSpeeds, TAL_speeds
- Results:
- Training graph: Cth_TAL_speeds_TrainingGraph
- Lap times and % success: Cth_TAL_speeds_Barplot
- Aim: Compare the baseline and TAL on different maps with a maximum speed of 6 m/s.
- Config file: Cth_maps, TAL_maps
- Results:
- Training graphs: TAL_Cth_maps_TrainingGraph
- Lap times and success bar plot: TAL_Cth_maps_Barplot
- Aim: Study the speed profiles
- Requires the pure pursuit (PP_speeds) results
- Results:
- Trajectories: GenerateVelocityProfiles, set the folder to TAL_speeds
- Speed profile pp TAL: TAL_speed_profiles
- Speed profile x3: TAL_speed_profiles
- Slip profile: TAL_speed_profiles
- Aim: Compare our method with the literature
- Results:
- Bar plot: LiteratureComparison
- Note that the results from the literature are hard coded.
If you find this work useful, please consider citing:
@ARTICLE{10182327,
author={Evans, Benjamin David and Engelbrecht, Herman Arnold and Jordaan, Hendrik Willem},
journal={IEEE Robotics and Automation Letters},
title={High-Speed Autonomous Racing Using Trajectory-Aided Deep Reinforcement Learning},
year={2023},
volume={8},
number={9},
pages={5353-5359},
doi={10.1109/LRA.2023.3295252}
}