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An inverse game solver for inferring objectives from noise-corrupted partial state observations of non-cooperative multi-agent interactions.

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PRBonn/PartiallyObservedInverseGames.jl

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PartiallyObservedInverseGames.jl

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An inverse game solver for inferring objectives from noise-corrupted partial state observations of non-cooperative multi-agent interactions.

Paper

@inproceedings{peters2021rss,
    title     = {Inferring Objectives in Continuous Dynamic Games from Noise-Corrupted Partial State Observations},
    author    = {Peters, Lasse and Fridovich-Keil, David and Rubies-Royo, Vicen\c{c} and Tomlin, Claire J. and Stachniss, Cyrill},
    booktitle = {Proc.~of Robotics: Science and Systems (RSS)},
    year      = {2021},
    codeurl   = {https://github.com/PRBonn/PartiallyObservedInverseGames.jl},
    videourl  = {https://www.youtube.com/watch?v=BogCsYQX9Pc},
    url       = {https://arxiv.org/abs/2106.03611}
}

Setup

This code was tested with Julia versions 1.5 and 1.6.

Basic

Clone this reposistory

git clone https://github.com/PRBonn/PartiallyObservedInverseGames.jl

After you have cloned the repository, you can install all dependencies at the versions recorded in the Manifest.toml:

  1. Navigate to the installation directory, e.g. cd ~/.julia/dev/PartiallyObservedInverseGames
  2. Start Julia in project mode: julia --project
  3. Hit ] to enter package mode and run: pkg> instantiate

Finally, you can run the unit tests via ] test to confirm that the setup was successful. Now you are ready to use the package. See Directory Layout for further details.

Binary Data Version Control

Beyond that, we use DVC for binary data version control. This part of the setup is only required if you want to load our results as binary data rather than reproducing them yourself by re-running the experiments. DVC can be installed as follow:

  1. Install dvc with http support, e.g. pip install "dvc[http]"
  2. [Optional] Setup git hooks to automate the process of checking out dvc-controlled files: dvc install

Now you can download the binary data and figures by running dvc pull.

Directory Layout

  • src/ contains the implementations of our method and the baseline for inverse planning. Beyond that it contains implementations of forward game solvers and visualization utilities.

  • test/ contains unit and integration tests for the code in src/

  • experiments/ contains the code for reproducing the Monte Carlo study for the running example (experiments/unicycle.jl) and the highway overtaking scenario (experiments/highway.jl).

  • After setting up dvc as described above and running dvc pull the directory data/ contains the binary data of our results (as .bson file) as well as their visualization (as .pdf file).

Reproducing Results

The results of the Monte Carlo study can be reproduced by running the corresponding scripts in experiments/:

  • 2-Player running example of collision avoidance: experiments/unicycle.jl
  • 5-Player highway overtaking scenario: experiments/highway.jl

Caching

The scripts located in experiments/ will check for cached results in data/. If cached results are found, they will be loaded and the figures will be reproduced from this data. In order to reproduce results from scratch you will have to clear the cache first by calling clear_cache!() (implemented in experiments/utils/simple_caching.jl). Alternatively, you can remove the @run_cached macro in front the function calls in the experiment to disable caching for that call.

Distributed Experiments

Running a large scale Monte Carlo study can take a substantial amount of time. Thus, this package uses Distributed.jl for parallelization. If there are multiple workers registered in the worker pool, the experiment scripts will automatically parallelize since all heavy lifting is implemented using Distributed.pmap. Workers can run on the same machine, on a remote cluster, or even both. The only requirement is that all code can be loaded on the remote worker. This can be achieved by mounting the repository to a shared directory that is available from all nodes in the (potentially heterogeneous) cluster or by utilizing rsync. A suit of useful utility functions for this task can also be found in Distributor.jl.

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An inverse game solver for inferring objectives from noise-corrupted partial state observations of non-cooperative multi-agent interactions.

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