Forward invariance is a long-studied property in control theory that is used to certify that a dynamical system stays within some pre-specified set of states for all time, and also admits robustness guarantees (e.g., the certificate holds under perturbations). We propose a general framework for training and provably certifying robust forward invariance in Neural ODEs. We apply this framework to provide certified safety in robust continuous control. To our knowledge, this is the first instance of training Neural ODE policies with such non-vacuous certified guarantees. In addition, we explore the generality of our framework by using it to certify adversarial robustness for image classification.

We propose a method for training ordinary differential equations by using a control-theoretic Lyapunov condition for stability. Our approach, called LyaNet, is based on a novel Lyapunov loss formulation that encourages the inference dynamics to converge quickly to the correct prediction. Theoretically, we show that minimizing Lyapunov loss guarantees exponential convergence to the correct solution and enables a novel robustness guarantee. We also provide practical algorithms, including one that avoids the cost of backpropagating through a solver or using the adjoint method. Relative to standard Neural ODE training, we empirically find that LyaNet can offer improved prediction performance, faster convergence of inference dynamics, and improved adversarial robustness. Our code available at https://github.com/ivandariojr/LyapunovLearning .

Decision trees are ubiquitous in machine learning for their ease of use and interpretability; however, they are not typically implemented in reinforcement learning because they cannot be updated via stochastic gradient descent. Traditional applications of decision trees for reinforcement learning have focused instead on making commitments to decision boundaries as the tree is grown one layer at a time. We overcome this critical limitation by allowing for a gradient update over the entire tree structure that improves sample complexity when a tree is fuzzy and interpretability when sharp. We offer three key contributions towards this goal. First, we motivate the need for policy gradient-based learning by examining the theoretical properties of gradient descent over differentiable decision trees. Second, we introduce a regularization framework that yields interpretability via sparsity in the tree structure. Third, we demonstrate the ability to construct a decision tree via policy gradient in canonical reinforcement learning domains and supervised learning benchmarks.

This provides one way of leveraging and combining the advantages of model-free and model-based approaches. Specifically, we differentiate through MPC by using the KKT conditions of the convex approximation at a fixed point of the controller. Using this strategy, we are able to learn the cost and dynamics of a controller via end-to-end learning. Our experiments focus on imitation learning in the pendulum and cartpole domains, where we learn the cost and dynamics terms of an MPC policy class. We show that our MPC policies are significantly more data-efficient than a generic neural network and that our method is superior to traditional system identification in a setting where the expert is unrealizable.