You are welcome. Figure 1 in [2] is the diagram I was thinking
of.
On 03-Nov-15 20:39, Tobias Pfeiffer
wrote:
This helps very much, thank you for taking the time to answer!
You might be looking for for "Combining Online and
To make matters more difficult I assume that this also depends on the exact
node evaluation you’re using. There’s UCT + RAVE, then there’s just RAVE
(as used by Michi). And then you can add other things in there as well like
criticality (like Pachi, and at least at one point CrazyStone).
I
The name "Monte Carlo" strongly seems to suggest, that randomness it at the
core of the method. And randomness does play a role.
But what really happend in the shift to MC, was that bots didn't try to
evaluate intermediate positions anymore. Instead, all game knowledge was
put into selecting
Hi everyone,
I haven't yet caught up on most recent go papers. If what I ask is
answered in one of these, please point there.
It seems everyone is using quite heavy playouts these days (nxn
patterns, atari escapes, opening libraris, lots of stuff that I don't
know yet, ...) - my question is how
(for example) both
attack and defense moves in a situation.
David
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of
Tobias Pfeiffer
Sent: Tuesday, November 03, 2015 12:39 PM
To: r...@ffles.com; computer-go@computer-go.org
Subject: Re: [Computer-go] AMAF/RAVE + heavy
You have to be careful what heuristics you apply. This was a
surprising result: using a playout policy which in itself is a
stronger go player can actually make MCTS/AMAF weaker. The reason
is that MCTS depends entirely on accurate estimations of the value
of
This helps very much, thank you for taking the time to answer!
You might be looking for for "Combining Online and Offline Knowledge in
UCT" [1] by Gelly and Silver. Silver Tesauroreference it in "Monte-carlo
Simulation Balancing" [2] with "Unfortunately, a stronger simulation
policy can actually
