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Probabilistic Recurrent State-Space Models

This is the companion code for the dynamics model learning method reported in the paper Probabilistic Recurrent State-Space Models by Andreas Doerr et al., ICML 2018. The paper can be found here https://arxiv.org/abs/1801.10395. The code allows the users to reproduce the PR-SSM results reported in the benchmark and large-scale experiments. Please cite the above paper when reporting, reproducing or extending the results.

Purpose of the project

This software is a research prototype, solely developed for and published as part of the publication cited above. It will neither be maintained nor monitored in any way.

Requirements, how to build, test, install, use, etc.

The PR-SSM code depends on Tensorflow.

Prerequesits

In order to train a PR-SSM model for a new dataset, a new task has to be derived from the task base class. See for example real_world_tasks.py.

A valid path must be provided to store the experimental results and log files. An example is given in run_benchmark_experiments.py.

Reproducing PR-SSM results

The experiments reported in the publication can be run by executing

python benchmarks/run_real_world_tasks/run_benchmark_experiments.py
python benchmarks/run_real_world_tasks/run_large_scale_experiment.py

The individual datasets have to be provided in the datasets folder.

License

Probabilistic recurrent state-space models is open-sourced under the MIT license. See the LICENSE file for details.

For a list of other open source components included in Benchmarks, see the file 3rd-party-licenses.txt.

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Python implementation of the PR-SSM.

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