Hi!
On Wed, Mar 09, 2016 at 04:43:23PM +0900, Hiroshi Yamashita wrote:
> AlphaGo won 1st game against Lee Sedol!
Well, I have to eat my past words - of course, there are still four
games to go, but the first round does not look like a lucky win at all!
Huge congratulations to the AlphaGo team, you have done truly amazing
work, with potential to spearhead a lot of further advances in AI in
general! It does seem to me that you must have made a lot of progress
since the Nature paper though - is that impression correct?
Do you have some more surprising breakthroughs and techniques in store
for us, or was the progress mainly incremental, furthering the training
etc.?
By the way, there is a short snippet in the paper that maybe many
people overlooked (including me on the very first read!):
> We introduce a new technique that caches all moves from the search
> tree and then plays similar moves during rollouts; a generalisation of
> the last good reply heuristic. At every step of the tree traversal, the
> most probable action is inserted into a hash table, along with the
> 3 × 3 pattern context (colour, liberty and stone counts) around both the
> previous move and the current move. At each step of the rollout, the
> pattern context is matched against the hash table; if a match is found
> then the stored move is played with high probability.
This looks like it might overcome a lot of weaknesses re semeai etc.,
enabling the coveted (by me) information flow from tree to playouts, if
you made this to work well (it's similar to my "liberty maps" attempts,
which always failed though - I tried to encode a larger context, which
maybe wasn't good idea).
Would you say this improvement is important to AlphaGo's playing
strength (or its scaling), or merely a minor tweak?
Thanks,
--
Petr Baudis
If you have good ideas, good data and fast computers,
you can do almost anything. -- Geoffrey Hinton
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