Deep Reinforcement learning is a promising approach for autonomous robots and has been utilized in Autonomus Driving. This would be end-to-end trainable though.
Network architecture (Agent)
TBD
Imitation Learning: Alternative approach, learn the driving strategy end-to-end from recorded data
Simulation Environment:
Gazebo Simulation, realism could be higher, but integrates all of our ROS inputs directly, so directly reusable in the real world. LIDAR and cameras as well as the side switch are simulated.
Other publicly available simulators with custom environments: AirSim or Carla
Constraints:
The network has to run at least at 25FPS on our on-board hardware (A Jetson TX2 )
This also includes communication with the ROS core running on the main Intel NUC board
Tasks:
[ ] Setup Gazebo simulation
[ ] Research Deep Reinforcement Learning.
[ ] Try out different approaches in the Simulation
[ ] Benchmark on the car to measure performance and detect problems
Objective: Train a network to control the car
Deep Reinforcement learning is a promising approach for autonomous robots and has been utilized in Autonomus Driving. This would be end-to-end trainable though.
Network architecture (Agent)
Simulation Environment:
Constraints:
Tasks:
Resources:
https://github.com/simoninithomas/Deep_reinforcement_learning_Course