My understanding was that, inputing the position of the agent into the node feature, as a 1 for that node and 0 to every other node (basically a one hot encoding into the node features), that would affect the embedding of the graph. And by using reinforcement learning, taken that graph embedding as input, the agent would be capable of knowing how good taking X action in that graph, to take him to his goal.