TrajectoryAidedLearning

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.

Result Generation

The results in the paper are generated through a two step process of:

1. Train and test the agents
2. Process and plot the data

For every test:

• Run calculate_statistics
• Run calculate_averages

Tests:

Maximum Speed Investigation

• 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

6 m/s Performance Comparision

• 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

Speed Profile Analysis

• 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

Comparison with Literatures

• Aim: Compare our method with the literature
• Results:
  • Bar plot: LiteratureComparison
• Note that the results from the literature are hard coded.

Citation

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}
}