Conflict resolution for multiple vehicles in confined spaces
Xu Shen (xu_shen@berkeley.edu), Francesco Borrelli
Project Webpage: https://bit.ly/rl-cr
- Clone this repo
- Run
pip install -e .
in the root level of this repo (A virtualenv is recommended). However, due to some buggy dependencies between package versions, you need to manually switch the following packages to specific versions (while ignoring the warning given by the pip dependency resolver):pip install pettingzoo==1.20.1
pip install supersuit==3.5.0
pip install stable-baselines3==1.6.0
pip install gym==0.25.0
- A pretrained model can be downloaded here.
- Run
python confrez/rl/experiment.py
to see the steps taken by the DQN policy to resolve the conflict in the discrete environment. - Run
python confrez/rl/record_states_history.py
to generate a.pkl
file that records the steps taken by the RL agents, which will serve as the configuration strategies for the trajectory planning problems. - Run
python confrez/control/vehicle.py
to plan a single-vehicle collision free trajectory following the strategy-guided configurations. - (Centralized method) Run
python confrez/control/multi_vehicle_planner.py
to solve the multi-vehicle trajectory planning problem to resolve conflict. - (Distributed method) Run
python confrez/control/vehicle_follower.py
so that each vehicle generates its own strategy-guided reference trajectory, and then follows it with distributed MPC to avoid collisions.
- Run
python confrez/rl/train.py
to train a new policy. - You may set the
random_reset
argument toTrue
so that you train the policy with a random subset out of 4 vehicles, which may lead to a more generalizable policy. But it also require longer training time. - Different random seeds and different parameter tuning will lead to different behaviors in the trained policy. With your new policy, the vehicles may (almost for sure) take different actions and resolve the conflict in another way.