Note: I developed this project as part of a challenge project suggested by Dr. Alfredo Canziani in my Deep Learning course at New York University. I took the initial code that sets up a simulated environment, an agent (truck), and the agent’s dynamics within the environment from here. I designed/developed the training process and built two models that learn to back up the agent to the target position from any initial point on their own. The training process, the agent's behavior before and after the training, and the instructions for how to run the code are explained below.

Truck Kinematics

Training Emulator

Training Controller

Simulation Before Training

Simulation After Training

Simulation Beyond Training Boundaries

Trajectories

Run the Simulation

Create and activate the conda environment with:

conda env create -f conda_env.yaml

conda activate truck_backer_upper

To test the models inside the training region, run:

python truck-backer-upper.py

To test the models outside the training region, run:

python truck-backer-upper.py\
    --env_x_range 0 100\
    --env_y_range -30 30\
    --test_x_cab_range 40 90\
    --test_y_cab_range -25 25

To train both emulator and controller models, run:

python truck-backer-upper.py --train_emulator True 

To train only controller models, run:

python truck-backer-upper.py --train_controller True 

References

• Nguyen, D., & Widrow, B. (1989). The truck backer-upper: an example of self-learning in neural networks. In International 1989 Joint Conference on Neural Networks (pp. 357–363, vol. 2). https://doi.org/10.1109/IJCNN.1989.118723

• Schoenauer, M., & Ronald, E. (1994). Neuro-genetic truck backer-upper controller. In Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence (pp. 720–723, vol. 2). https://doi.org/10.1109/ICEC.1994.349969