The control goal is to navigate a robot to a goal region while avoiding an obstacle.
Time horizon: t = 6s. Control period: 0.2s.
Initial states:
x1 = [2.9, 3.1]
x2 = [2.9, 3.1]
x3 = [0, 0]
x4 = [0, 0]
Dynamic system: dynamics.m
Goal region ( t=6 ):
x1 = [-0.5, 0.5]
x2 = [-0.5, 0.5]
x3 = [-Inf, Inf]
x4 = [-Inf, Inf]
Obstacle ( always ):
x1 = [1, 2]
x2 = [1, 2]
x3 = [-Inf, Inf]
x4 = [-Inf, Inf]
We provide two networks:
- The first network is trained with standard (point-based) reinforcement learning:
nn-nav-point.onnx - The second network is trained set-based to improve its verifiable robustness by integrating reachability analysis into the training process:
nn-nav-set.onnx
Reference set-based training: https://arxiv.org/abs/2401.14961