1 code implementation • 20 Oct 2022 • Ilya Orson Sandoval, Panagiotis Petsagkourakis, Ehecatl Antonio del Rio-Chanona
Neural ordinary differential equations (Neural ODEs) define continuous time dynamical systems with neural networks.
no code implementations • 30 Jul 2020 • Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford, Federico Galvanin, Dongda Zhang, Ehecatl Antonio del Rio-Chanona
We propose a chance constrained policy optimization (CCPO) algorithm which guarantees the satisfaction of joint chance constraints with a high probability - which is crucial for safety critical tasks.
no code implementations • 4 Jun 2020 • Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford, Dongda Zhang, Ehecatl Antonio del Río Chanona
We use chance constraints to guarantee the probabilistic satisfaction of process constraints, which is accomplished by introducing backoffs, such that the optimal policy and backoffs are computed simultaneously.
2 code implementations • 15 Apr 2019 • Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford, Dongda Zhang, Ehecatl Antonio del Rio Chanona
In this work, we applied the Policy Gradient method from batch-to-batch to update a control policy parametrized by a recurrent neural network.
Optimization and Control Systems and Control