Code for paper:
"PAC-Bayes Control: Synthesizing Controllers that Provably Generalize to Novel Environments"
Authors: Anirudha Majumdar and Maxwell Goldstein
Link to paper: https://arxiv.org/abs/1806.04225
The code here provides a complete implementation of the Relative Entropy Programming version of the approach presented in the paper on the obstacle avoidance example.
SCS solver: https://github.com/cvxgrp/scs
Running the code
The script main.py will run everything. In particular, it will:
Optimize the PAC-Bayes controller using Relative Entropy Programming.
Estimate the true expected cost for the optimized controller on novel environments in order to compare with the PAC-Bayes bound (In order to speed things up, we use a small number of environments here. A large number of samples should be used for a more accurate estimate, as is done in the paper.)
Visualize the controller running on a number of test environments.
Description of the other files:
optimize_PAC_bound.py: Solves the Relative Entropy Program for optimizing PAC-Bayes controllers.
utils_simulation.py: Contains a number of utility functions for performing simulations (e.g., generating obstacle environments, implementing the robot's dynamics, simulating the depth sensor, etc.).
compute_kl_inverse.py: Self-contained implementation for computing the KL inverse using Relative Entropy Programming.