Fig. 6
From: Selective network discovery via deep reinforcement learning on embedded spaces
NAC convergence behaviour with and without embedding. In a we see the impact of the large state space on the convergence rate when the agent does not use an embedding of the network state. Given the size of the state space, there is low probability that the agent will observe the same state multiple times, and therefore learning is much slower and less generalizable. In contrast, we observe in b, c how the convergence rate increases as the quality of the state embedding increases with respect to the target task