no code implementations • 29 Feb 2024 • Ather Gattami
For stable nonlinear systems, we show that the algorithm converges and that the converging parameters of the trained neural network can be made arbitrarily close to the optimal neural network parameters.
no code implementations • 27 Jan 2023 • Johan Östman, Ather Gattami, Daniel Gillblad
We consider a decentralized multiplayer game, played over $T$ rounds, with a leader-follower hierarchy described by a directed acyclic graph.
no code implementations • 10 Jun 2020 • Qinbo Bai, Vaneet Aggarwal, Ather Gattami
This paper uses concepts from constrained optimization and Q-learning to propose an algorithm for CMDP with long-term constraints.
no code implementations • 11 Mar 2020 • Qinbo Bai, Vaneet Aggarwal, Ather Gattami
The proposed algorithm is proved to achieve an $(\epsilon, p)$-PAC policy when the episode $K\geq\Omega(\frac{I^2H^6SA\ell}{\epsilon^2})$, where $S$ and $A$ are the number of states and actions, respectively.
no code implementations • 18 Feb 2020 • Hanwei Wu, Ather Gattami, Markus Flierl
One challenge of using deep learning models for finance forecasting is the shortage of available training data when using small datasets.
no code implementations • 23 Jan 2019 • Ather Gattami
We introduce a game theoretic approach to construct reinforcement learning algorithms where the agent maximizes an unconstrained objective that depends on the simulated action of the minimizing opponent which acts on a finite set of actions and the output data of the constraint functions (rewards).