Peer review

[giogenna16]

Hi,
Overall, I think you did a good job because the algorithm works and finds solutions that seem to be good, near the optimum!
The README file is short, but clear; the code is not too difficult to understand, but a few comments might help.
The choice that caught my attention the most is the fact that, I think, you do a sort of initialization for the initial state (like an heuristic to fix something and reduce the computations (and the execution time), at the expense of a maybe worse solution): 'initial_state = State(np.array([input_state[0]]))'; I think that this should be like to impose a constraint in the research of the solution (but I do not know if this is wanted). Without initialization, so 'initial_state = State(np.array([]))', for N=20, the weight of the reached solution is equal to 24 (the weight of the solutions obtained with Dijkstra or A* is 23), but, obviously, the nodes and the states increases, instead, with initialization it is equal to 27 (the result written in the README file).
I believe that there is a redundant control, but it is a minor: in possible_actions(state) function, you verify if is_valid(state, item), so, I think that it is not necessary the control if not is_valid(state, a): continue, in search(...) function.
It is interesting the fact that the initial changes done on input_state delete the duplicates, so optimize the research: for example, passing, for n=5, from 25 lists to 10 tuples.