The modeling of ideal transition function inI2Q isfully decentralized and independent from the learned policies of other agents, helping I2Q be free from non-stationarity and learn the optimal policy.

One critical flaw of voting is its susceptibility to strategic manipulations. That is, agents may have an incentive to misreport their preferences (i.e.

Unlike AC, DAC can dynamically adjust the algorithm's configuration during

Modeling multi-agent systems requires understanding howagents interact. Such systems are often difficult to model because they can involve a variety of types ofinteractions that layer together todriverich social behavioral dynamics.

To test the efficiency of each component, we remove them separately (LG-ODE-no att,7 LG-ODE-no PE) and find the performances drop. This suggests that distinguishing the importance of nodes w.r.t8 time and incorporating temporal information via learnable positional encoding would benefit model performance.9 ForEqn2, we adopt the GNN model in[2]tocapture the interaction among agents.

In contrast, the mathematics needed to analyze such schemes is what forms the focus in Stochastic Approximation (SA) theory [2, 4]. More generally, SA refers to an iterative scheme that helps find zeroes or optimal points of a function, for which only noisy evaluationsarepossible.