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Implementation of Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation

Final project for CISC 856: Reinforcement learning

-Simulation enviornment: Carla 0.9.13 (Town02 map)

ProjectDemo.mp4


a) Simulation enviornment b) On-board camera

Abstract

We implemented a self-supervised deep reinforcement learning algorithm on a simulated vehicle to learn a collision-free navigation policy. This algorithm is a generalized version of model-free and model-based learning that allows for a more robust system which uses aspects of both learning techniques. By using a list of generated actions and an image taken from a simulated camera on-board the vehicle, the probability of collision for each time step is generated and used to navigate through an unstructured environment. After implementing and testing the algorithm under different conditions, the model had difficulties learning and produced sub-optimal results. Some improvements for future work on the system were identified which are planned to be pursued by all group members.

Code List

RL_project_main.py

gcg.py

Code to implement the generalized computation graph which uses a Recurrent Neural Network (RNN) for the task of collision avoidance over a short predictive horizon.

RL_funcs.py

config.py

Carla_funcs.py

How to Run

  1. Download CARLA Simulator from https://carla.org/ (The project was implemented using release 0.9.13 for Windows and 0.9.12 for Ubuntu)
    • Ensure CARLA Simulator Python API requirements are met in your environment (check \CARLA_0.9.XX\PythonAPI\carla\requirments.txt)
  2. It is highly recommended that you execute the code using a GPU. The code was setup for TensorFlow-gpu 2.3.1 so that the Numpy version requirements could be met for both the CARLA Simulator and TensorFlow
    • for a CPU implementation (for training) ensure the project's dependencies are met using project_requirments.txt
    • for a GPU implementation (for training) ensure the project's dependencies are met using carla.yml
  3. Copy the PythonAPI and all subfolders into your project directory (in parallel with our code directory)
  4. Launch the CARLA Server (rendering off screen is recommended) from your CARLA install
    • CarlaUE4.exe -RenderOffScreen for Windows
    • ./CarlaUE4.sh - RenderOffScreen for Ubuntu
  5. Launch your virtual environment (if you're using one)
  6. Configure the CARLA server to run Town02 map from the code subfolder in your project directory
    • python config.py -m Town02
  7. Run our main code:
    • python Rl_Project_main.py

Contributors

Riley Cooper - Electrical and Computer Engineering
Jason Harris - Electrical and Computer Engineering
Liam Horton - Mechanical and Materials Engineering