<I discussed this with Dr. Professor Edi and he confirmed that although the formulation allows for the representation of *some* directed graphs, it cannot represent *arbitrary* directed graphs, so it won’t properly represent dynamics in general… although its probably better than trying to shoehorn an undirected approach for a directed graph. Fundamentally, for the general case, you get complex results from the eigen decomposition.>

- Graph Laplacian for directed graphs, where adjacent (in one direction) states may have very different value functions.
- “We provide an analysis using the Dirichlet sum of the graph Laplacian to show how the smoothness of the basis functions is affected by the graph’s invariant distribution.”
- Standard graph laplacian only works when weights are symmetric
- Graph must be strongly connected and aperiodic – pretty mild assumptions
- Equations for the combinatorial and normalized directed Laplacians are not terribly complex
- <I take that back – it needs something called the Perron vector of the transition matrix, but it requires this weird manipulation that pushes the actual transition distribution in the direction of the uniform distribution. Its not totally clear if this always has to be done, or just in special-case graphs. Need to learn more before I make a decision about it. Here its computed by an iterative update.>

- <Its not yet clear to me how much this actually fixes the problem – would have to spend more time, because in the end you still create an undirected graph, which seems like it shouldn’t be able to represent everything in a directed graph?>
- “The undirected Laplacian is a special case of the directed Laplacian.”
- Test on 2-room gridworld (where rooms are connected by directional doors), inverted pendulum (which is done by basically keeping track of balls around sampled points)
- <Results aren’t very impressive – performance in pendulum is flat for 30-50 basis functions, and is about 20% below optimal.>

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