SupplementaryMaterialfor" HierarchicalAdaptive ValueEstimationforMulti-modalVisual ReinforcementLearning "

Section C describes the details of the experimental setup, including network architectures, hyperparameters,andhardwaredetails. Thisoutcomeemphasizes the necessity of feature interaction or feature fusion to tackle intricate situations. Furthermore, an amalgamation of feature fusion and value fusion can offer better performance. This adjustment allows us to evaluate the robustness and adaptability of our approach in handling a larger number of vehicles in the environment. As we increase the number of vehicles on the road, Fig. A2 (a) clearly indicates that HAVE consistently delivers the highest performance. The training and testing curves of HAVE and other comparable methods are given in A4.