Solving Multi-Entity Robotic Problems Using Permutation Invariant Neural Networks

The author proposes a decentralized control system using permutation invariant neural network policies trained in simulation to address scalability limitations and heuristics reliance in multi-agent control strategies. The approach allows for autonomous determination of entity importance without bias or capacity constraints.