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 layer used by AlphaGo into its ANNs and then the decoding methods for the output layer, is likely not feasible for anything less than a Google sized team and investment; i.e half a dozen people and millions of dollars of computational access on their Google Cloud distributed computing architecture. Amazon (with AWS) and Microsoft (with Azure) are the only two other companies that would have the excess capacity in both supporting the personnel and the distributed computation costs. A distant second would be companies like IBM who could carry the personnel while leveraging AWS, Azure, Google Cloud, etc.
Once there is a sufficient investment in this kind of evolutionary meta-modeling, it will be a very useful starting point for others. However, until someone is willing to play extremely long term and pony up the HUGE up front costs of evolutionary bootstrapping out of the simple models NEAT handles today, it is a short-term dead in. On Fri, Feb 24, 2017 at 2:39 AM, Minjae Kim <xive...@gmail.com> wrote: > 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. For a complex enough game as go, one can expect that the search > space for the policy function would not be smooth at all. So supposedly > supervised learning was necessary to guide the policy function to a good > starting point before reinforcement. Without such, applying reinforcement > learning directly to a random policy can easily make the policy stuck at a > bad local optimum. I could have a miunderstanding at this point; correct me > if so, but to continue on: 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. I've had a look on NEAT and HyperNEAT, which are evolutionary > methods. Do these evolutionary algorithms scale well on complex strategic > decision processes and not just on simple linear decisions such as food > gathering and danger avoidance? In case not, what alternatives are known? > Is there any success case of a chess, go, or any kind of complex strategic > game playing algorithm, where it gained expert strength without domain > knowledge such as expert game examples? > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go >
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