<jupyter_start><jupyter_text>MCTS3 100000 quickly uses a gig of memory, MCTS4 does not. But it's also 4 times slower<jupyter_code>b = main.make_future_board(['G11', 'G9', 'F13', 'F11', 'F12', 'E10', 'F14', 'G12', 'G16', 'E14', 'E12', 'E13', 'C15', 'G8', 'G14'])
bigplayer3.make_move(b)<jupyter_output>G13
NE S NW N 
D11
W S E E 
G13
E14
SW 
G13
G11
NE N NE 
B15
C17
A13
A11
A13 0.3365758299827576 5
A14 0.33305745124816893 5
A15 0.3202573597431183 5
A16 0.3236416757106781 5
A17 0.3254283845424652 5
B13 0.34099743366241453 5
B14 0.3301449537277222 5
B15 0.3701402119227818 21
B16 0.2897437632083893 5
B17 0.34501931816339493 4
C13 0.3718564756538557 23
C14 0.33155436175210135 7
C16 0.31902716755867006 5
C17 0.28678339123725893 5
D13 0.3245304822921753 5
D14 0.3353999018669128 5
D15 0.3087945878505707 5
D16 0.33373887538909913 5
D17 0.3376229465007782 5
E15 0.3228562891483307 5
E16 0.33145266175270083 5
E17 0.3282972157001495 5
C8 0.3324337124824524 5
C9 0.3346347451210022 5
C10 0.3315001130104065 5
C11 0.33057333827018737 5
C12 0.33433382511138915 5
D8 0.33660273551940917 5
D9 0.3343940258026123 5
D10 0.32981722950935366 5
D11 0.3337409973144531 5
D12 0.32633307576179504 5
E8 0.33940579891204836 5
E9 0.3285154104232788 3
E11 0.33085635900497434 5
F8 0.34838253259658813 5
F9 0.29475814700[...]
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