A Neural Network Approach Applied to MultiAgent Optimal Control
We propose a neural network approach for solving highdimensional optimal control problems. In particular, we focus on multiagent control problems with obstacle and collision avoidance. These problems immediately become highdimensional, even for moderate phasespace dimensions per agent. Our approach fuses the Pontryagin Maximum Principle and HamiltonJacobiBellman (HJB) approaches and parameterizes the value function with a neural network. Our approach yields controls in a feedback form for quick calculation and robustness to moderate disturbances to the system. We train our model using the objective function and optimality conditions of the control problem. Therefore, our training algorithm neither involves a data generation phase nor solutions from another algorithm. Our model uses empirically effective HJB penalizers for efficient training. By training on a distribution of initial states, we ensure the controls' optimality is achieved on a large portion of the statespace. Our approach is gridfree and scales efficiently to dimensions where grids become impractical or infeasible. We demonstrate our approach's effectiveness on a 150dimensional multiagent problem with obstacles.
 Award ID(s):
 1751636
 Publication Date:
 NSFPAR ID:
 10232667
 Journal Name:
 European Control Conference
 Sponsoring Org:
 National Science Foundation
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