gym-collision-avoidance

This is the code associated with the following paper:

M. Everett, Y. Chen, and J. P. How, "Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018

• Paper: https://arxiv.org/abs/1805.01956
• Video: https://www.youtube.com/watch?v=XHoXkWLhwYQ

This repo also contains the trained policy for the SA-CADRL paper (referred to as CADRL here) from the proceeding paper:

Yu Fan Chen, Michael Everett, Miao Liu, and Jonathan P. How. “Socially Aware Motion Planning with Deep Reinforcement Learning.” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Vancouver, BC, Canada, Sept. 2017.

About

In this multiagent environment, agents try to get to their own goal location (specified at the start of each episode) by using one of many collision avoidance policies implemented. Episodes end when agents reach their goal, collide, or timeout. Agents can observe the environment through one of many types of sensors, including one which provides raw state info about other agents.

Objective: Provide a flexible codebase, reduce time spent re-implementing existing works, and establish baselines for multiagent collision avoidance problems.

Install

Grab the code from github, initialize submodules, install dependencies and src code

git clone --recursive [email protected]:mit-acl/gym-collision-avoidance.git # If internal to MIT-ACL, use GitLab origin instead
cd gym-collision-avoidance
./install.sh

Minimum working example

To simulate a 2-agent scenario:

./example.sh

This will save a plot in gym_collision_avoidance/experiments/results/example so you can visualize the agents' trajectories.

You can use example.py as a starting point to write code for this environment.

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Further experiments

To replicate experiments in submitted IJRR paper:

Formations (spelling out CADRL):

./gym_collision_avoidance/experiments/run_cadrl_formations.sh

This will run 10 test cases and save plots and animations (.gif and .mp4) format in gym_collision_avoidance/experiments/results/cadrl_formations.

To collect a dataset of trajectories:

./gym_collision_avoidance/experiments/run_trajectory_dataset_creator.sh

This will store pngs of the trajectories and a .pkl file of relevant data from the trajectories in the experiments/results/trajectory_dataset folder. The resulting dataset could be used to train predictive models, initialize an RL agent's policy, etc. You can change the test_case_fn to use different scenarios, the policies dict to give agents different policies, etc.

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Common Issues

On OSX

RuntimeError: Python is not installed as a framework. The Mac OS X backend will not be able to function correctly if Python is not installed as a framework.

Add a line backend: TkAgg to ~/.matplotlib/matplotlibrc.

error: Cannot compile MPI programs. Check your configuration!!!:

brew install mpich

error with matplotlib and freetype not being found:

brew install pkg-config

To update the Python-RVO2 source code and re-generate the rvo2 python library, the results won't have any effect unless you remove the build dir:

# enter the venv
cd gym-collision-avoidance/gym_collision_avoidance/envs/policies/Python-RVO2
rm -rf build && python setup.py build && python setup.py install

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Policies Implemented

Learning-based:

• SA-CADRL: Socially Aware Motion Planning with Deep Reinforcement Learning
• GA3C-CADRL: Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
• DRL_Long: Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning

Classical:

• RVO/ORCA: Python-RVO2
• Non-Cooperative (constant velocity toward goal position)
• Static (zero velocity)

Desired Additions:

• DWA
• Social Forces
• Additional learning-based methods
• Other model-based methods
• Centralized planners

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TODOs:

• Get DRLLong working by pointing to correct commit on mfe's fork (cuda in python)
• Add test suite code to README
• Confirm sensors work
• Get GA3C-CADRL to actually use the dict obs?

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If you find this code useful, please consider citing:

@inproceedings{Everett18_IROS,
  address = {Madrid, Spain},
  author = {Everett, Michael and Chen, Yu Fan and How, Jonathan P.},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  date-modified = {2018-10-03 06:18:08 -0400},
  month = sep,
  title = {Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning},
  year = {2018},
  url = {https://arxiv.org/pdf/1805.01956.pdf},
  bdsk-url-1 = {https://arxiv.org/pdf/1805.01956.pdf}
}