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# lubaroli / disco

Code for the ICRA paper DISCO: Double Likelihood-free Inference Stochastic Control

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# DISCO: Double Likelihood-free Inference Stochastic Control

Abstract: Accurate simulation of complex physical systems enables the development, testing, and certification of control strategies before they are deployed into the real systems. As simulators become more advanced, the analytical tractability of the differential equations and associated numerical solvers incorporated in the simulations diminishes, making them difficult to analyse. A potential solution is the use of probabilistic inference to assess the uncertainty of the simulation parameters given real observations of the system. Unfortunately the likelihood function required for inference is generally expensive to compute or totally intractable. In this paper we propose to leverage the power of modern simulators and recent techniques in Bayesian statistics for likelihood-free inference to design a control framework that is efficient and robust with respect to the uncertainty over simulation parameters. The posterior distribution over simulation parameters is propagated through a potentially non-analytical model of the system with the unscented transform, and a variant of the information theoretical model predictive control. This approach provides a more efficient way to evaluate trajectory roll outs than Monte Carlo sampling, reducing the online computation burden. Experiments show that the controller proposed attained superior performance and robustness on classical control and robotics tasks when compared to models not accounting for the uncertainty over model parameters.

Welcome to DISCO's development page! DISCO is an open-source package for Python 3 providing modules and examples to quickly setup and run experiments with the control framework presented on our research paper.

This package is still under development. We are open to potential collaborations.

## Installation

DISCO is built for Python (version 3.6 or later), and depends on PyTorch, OpenAI Gym, NumPy, matplotlib, and SciPy.

Additionally, we use Dill to save session files for experiments. If these dependencies are not installed, you may install them with:

  $pip install -r requirements.txt$ pip install -r optional-requirements.txt

  $sudo pip install disco-rl ### From source $ git clone https://github.com/lubaroli/disco
$cd disco/$ sudo python setup.py install

## Running Experiments

For convenience, scripts for running the Pendulum experiment found on the paper are provided on both for Python and Jupyter. These files are located on the demo/ folder.

Although the Jupyter notebook describes the step-by-step design of the experiment, there are a few variables worth highlighting. The simulation is configured using the following variables:

    ENV_NAME   (required) The name of the environment. Models are provided for
'Pendulum-v0', 'CartPole-v1' and a custom 'SkidSteer'.
INIT_STATE (required) The initial states for all episodes. A tensor of
appropriate dimensions.
ITERATIONS (required) The number of episodes executed in for each test case.
PRIOR      (optional) If using distributions over parameters of the forward
model, this is the distribution used during the first
epoch.
POSTERIOR  (optional) If using distributions over parameters of the forward
model, this is the refined distribution used during
the subsequent epochs.
RENDER     (optional) Controls whether the experiments should be rendered
by gym.
VERBOSE    (optional) Controls whether progress messages are printed to the
console.
SAVE       (optional) Controls whether the experiment data and plots are
saved to 'data/local/<date-time>' folder.


Furthermore, the test cases for baseline comparison are defined in a dictionary holding the **kwargs used by the forward model, controller and pyplot.

cases = {
"baseline": {
"model": {"uncertain_params": None, "params_dist": None},
"controller": {"params_sampling": "none"},
"plot_kwargs": {"color": "g", "label": r"Ground-truth: $\rho$"},
},  #...
}

Please refer to each module documentation for details on these arguments.

Code for the ICRA paper DISCO: Double Likelihood-free Inference Stochastic Control