OK, so let’s talk theory vs practice.
In theory, TD learning approaches asymptotic optimality when used with a full
state space. That is, if your RL model has one parameter for each state, then
TD will converge those parameters to the game theoretic values. There are some
pre-conditions, but
On 21/02/2017 16:11, "Ingo Althöfer" wrote:
> Dear UEC organizers,
>
> GCP wrote (on behalf of Leela):
>> I did not register for the UEC Cup. I seem to be in good company there,
>> sadly.
>
> do you have a few wild cards for strong late entries?
Posting on behalf of the UEC organizers:
Yes, and
NEAT and hyperNEAT are awesome when "evolving" fairly simple networks with
a very limited number of input and output dimensions. However, without
access to some serious computational power, scaling the NEAT method up to
the kind of level you would need for the current encoding methods for the
input
"seeing" is complex when the input is just a bunch of pixels. Terry McIntyre
Unix/Linux Systems Administration Taking time to do
it right saves having to do it twice.
On Friday, February 24, 2017 12:32 PM, Minjae Kim wrote:
But those video games have a very simple optimal policy. Con
I should point out that Reinforcement Learning is a relatively unimportant
part of AlphaGo, according to the paper. They only used it to turn the
move-prediction network into a stronger player (presumably increasing the
weights of the layer before SoftMax would do most of the job, by making the
hig
But those video games have a very simple optimal policy. Consider Super
Mario: if you see an enemy, step on it; if you see a whole, jump over it;
if you see a pipe sticking up, also jump over it; etc.
On Sat, Feb 25, 2017 at 12:36 AM, Darren Cook wrote:
> > ...if it is hard to have "the good sta
TD-gammon is regarded as a special case from the stochastic characteristics
of the backgammon game; it smoothens the search space for the value
function and the value function itself to a great degree compared to those
'static' games such as go. Experiments of applying TD learning to chess and
go w
> ...if it is hard to have "the good starting point" such as a trained
> policy from human expert game records, what is a way to devise one.
My first thought was to look at the DeepMind research on learning to
play video games (which I think either pre-dates the AlphaGo research,
or was done in pa
Neural networks always have a lot of local optima. Simply because they have a
high degree of internal symmetry. That is, you can “permute” sets of
coefficients and get the same function.
Don’t think of starting with expert training as a way to avoid local optima. It
is a way to start trainin
I've recently viewed the paper of AlphaGo, which has done gradient-based
reinforcement learning to get stronger. The learning was successful enough
to beat a human master, but in this case, supervised learning with a large
database of master level human games was preceded the reinforcement
learning
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