We prove the existence of this geometry for a wide class of real world games by exposing their temporal nature.

Then we can update the joint distribution for the L.H.S. withϱl = ϱ 12 by exchanging the22 We prove the triangle inequality by contradictions similar to iii). Each agent has to resolve to select the action from its discrete action space to move around. Themixingnetwork56 has one hyper-layer as described in QMIX with64 units. The optimizer to optimize the neural57 networks is "Adam". Each URL algorithm is deployed to learn different joint policies (Z = 1058 for MPE andZ = 20 for GRF) and mixing networks every time.

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.