{"action":"created","issue":{"url":"https://api.github.com/repos/glinscott/leela-chess/issues/610","repository_url":"https://api.github.com/repos/glinscott/leela-chess","labels_url":"https://api.github.com/repos/glinscott/leela-chess/issues/610/labels{/name}","comments_url":"https://api.github.com/repos/glinscott/leela-chess/issues/610/comments","events_url":"https://api.github.com/repos/glinscott/leela-chess/issues/610/events","html_url":"https://github.com/glinscott/leela-chess/issues/610","id":323367604,"number":610,"title":"Wider (larger) PUCT, not narrower","user":{"login":"ASilver","id":3589847,"avatar_url":"https://avatars0.githubusercontent.com/u/3589847?v=4","gravatar_id":"","url":"https://api.github.com/users/ASilver","html_url":"https://github.com/ASilver","followers_url":"https://api.github.com/users/ASilver/followers","following_url":"https://api.github.com/users/ASilver/following{/other_user}","gists_url":"https://api.github.com/users/ASilver/gists{/gist_id}","starred_url":"https://api.github.com/users/ASilver/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ASilver/subscriptions","organizations_url":"https://api.github.com/users/ASilver/orgs","repos_url":"https://api.github.com/users/ASilver/repos","events_url":"https://api.github.com/users/ASilver/events{/privacy}","received_events_url":"https://api.github.com/users/ASilver/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":9,"created_at":"2018-05-15T20:17:52Z","updated_at":"2018-05-18T11:13:13Z","closed_at":null,"author_association":"NONE","body":"GCP published data, which I do not doubt, that in head-to-head matches, a smaller PUCT value, with fewer moves but taken deeper, led to better results for the smaller PUCT value. The problem is that this will tend to reinforce its current choices as opposed to encouraging it to explore moves it might find surprising such as... tactics. \r\n\r\nI'd like to suggest the PUCT value actually be increased to allow it to test out moves or situations it does not master, and learn from them. I do not think seeing fewer moves, but deeper, is the ideal way to evolve and learn."},"comment":{"url":"https://api.github.com/repos/glinscott/leela-chess/issues/comments/390175363","html_url":"https://github.com/glinscott/leela-chess/issues/610#issuecomment-390175363","issue_url":"https://api.github.com/repos/glinscott/leela-chess/issues/610","id":390175363,"user":{"login":"Tilps","id":12046033,"avatar_url":"https://avatars3.githubusercontent.com/u/12046033?v=4","gravatar_id":"","url":"https://api.github.com/users/Tilps","html_url":"https://github.com/Tilps","followers_url":"https://api.github.com/users/Tilps/followers","following_url":"https://api.github.com/users/Tilps/following{/other_user}","gists_url":"https://api.github.com/users/Tilps/gists{/gist_id}","starred_url":"https://api.github.com/users/Tilps/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Tilps/subscriptions","organizations_url":"https://api.github.com/users/Tilps/orgs","repos_url":"https://api.github.com/users/Tilps/repos","events_url":"https://api.github.com/users/Tilps/events{/privacy}","received_events_url":"https://api.github.com/users/Tilps/received_events","type":"User","site_admin":false},"created_at":"2018-05-18T11:13:13Z","updated_at":"2018-05-18T11:13:13Z","author_association":"CONTRIBUTOR","body":"Idea to consider.  Tuning for strength might be a valid approach - but rather than tuning for strength at 800 nodes, we tune for strength at large number of nodes.\r\nI have  couple of ideas for why this would be justifiable, neither of which is perfectly convincing. But still seems suggestive that its a decent idea.\r\n1) We want the resulting net to scale well, its possible that the tuning conditions should be aspirational to get the best *value* out of training towards the goal of scalability.\r\n2) Training reinforcement is kind of about making 800 nodes more and more effective.  So the convergence over time should be towards that 'large nodes' behaviour - so the puct that is good for that, aligns well with the reinforcement goal.\r\n\r\nBased on some other numbers I've seen before, this justification would suggest raising training puct from 0.6 to at least 0.7, possibly 0.8 would be a good idea."}}
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