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Discrete-Time Distribution Steering using MDPs

The repo contains codebase for the paper "Discrete-Time Distribution Steering using Monte Carlo Tree Search", submitted to IEEE Robotics and Automation Letters.

Summary

We propose an online planning algorithm that solves the discrete-time distribution steering problem. Our algorithm finds an adequate state-feedback controller at every timestep in order to guide the distribution of the state. At every timestep, our algorithm builds a tree of trajectories for the sample-based distribution of the state. At every node, various alternative control laws are explored.

We further propose a novel distance metric for the space of distributions.

Code Organization

The organization of the code is quite straight-forward.

  • algorithm.py

    • Contains the implementation of our proposed online planning algorithm, along with supporting classes. Our algorithm is implemented as the MCTS class, which contains Algorithms 2-4 from the paper.

    • Runs the algorithms and collects the results. In this script, we specify the dynamics, the initial and target state distribution, the half-spaces to be used in the distance metric, and run our algorithm for a given number of steps.

  • baseline.py

    • Contains the implementation of the baseline algorithm

  • utils.py

    • Contains the implementation of our novel distance metric compute_heur_dist (Algorithm 1 in paper)

  • parameters.py

    • Sets the necessary parameters

Further Notes

If interested in replicating our paper's results, please just switch to the corresponding branch.