Abstract: Embodiments described herein provide systems and methods for solving and applying a multi-agent decision process. A system performs a process, where at each iterative step, the system determines policies for a plurality of agents that optimize respective reward values based on the plurality of costs, and the characteristics of the plurality of agents. The system simulates the multi-agent decision process using the determined policies, thereby generating respective reward values and aggregated resource contribution values. The system increments or decrements the plurality of costs based on the constraints and the aggregated resource contribution values. The system updates a final reward value based on the respective reward values. The system updates a final plurality of costs based on the plurality of costs. After performing the iterative step for a predetermined number of iterations, the system outputs the final reward value and the final plurality of costs.

Abstract: Embodiments provide a method for predicting agent actions for neural network based agents according to an intervention. The method includes obtaining a first agent action at a first time step and a first intervention generated according to an intervention policy. The method also includes generating, by the neural network based agent model, a predicted agent action conditioned on the first agent action and the first intervention. The method also includes generating, by a neural network based intervention model, a second intervention according to the intervention policy and conditioned on the first agent action, the first intervention, and the predicted agent action. The method further includes executing a second agent action according to an agent policy that incurs a reward based on the second intervention. The method further includes training the neural network based intervention model by updating parameters of the neural network based intervention model based on an expected return.