Hi, In the paper you only present results of UCT_RAVE with the MoGo default policy. Did you run tests with UCT_RAVE using "pure" random playouts too?
I'm curious because I've tried millions ( well, it feels that way ) of uses for AMAF in my code... but so far all of them have been proven useless, often yielding worse results. /Christian Nilsson On 6/23/07, Sylvain Gelly <[EMAIL PROTECTED]> wrote:
Hello all, We just presented our paper describing MoGo's improvements at ICML, and we thought we would pass on some of the feedback and corrections we have received. (http://www.machinelearning.org/proceedings/icml2007/papers/387.pdf) The way that we incorporate prior knowledge in UCT can be seen as a bayesian prior, and corresponds exactly to the dirichlet prior (more precisely to the beta prior as we here get binomials). The cumulative result is only given using the prior knowledge on top of RAVE, but it could have been done the other way round and give the same type of results. Each particular improvement is somehow independent of the others. On figure 5, the legend of horizontal axis should be m_prior rather than n_prior. All experiments (except the default policy) were played against GnuGo level 10, not level 8. Any other comments are welcome! Sylvain & David _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
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