Toolkit for learning driving models through maximum entropy inverse reinforcement learning, and autonomous vehicle's control through leverageing effects on human actions.
(Companion code to a paper presented at RSS 2016)
To visualize: ./vis {file_name}.pickle
To run an experiment ./run {world_name} where world_name can be any one of the worlds defined in world.py
To run an experiment with irl_ground world: ./run irl_ground
To run the IRL algorithm: ./irl.py data/*.pickle
- dynamics.py: This contains code for car dynamics.
- car.py: Relevant code for different car models (human-driven, autonomous, etc.)
- feature.py: Definition of features.
- lane.py: Definition of driving lanes.
- trajectory.py: Definition of trajectories.
- world.py: This code contains different scenarios (each consisting of lanes/cars/etc.).
- visualize.py: This contains the code for visualization (GUI).