{"action":"created","issue":{"url":"https://api.github.com/repos/matthiasplappert/keras-rl/issues/161","repository_url":"https://api.github.com/repos/matthiasplappert/keras-rl","labels_url":"https://api.github.com/repos/matthiasplappert/keras-rl/issues/161/labels{/name}","comments_url":"https://api.github.com/repos/matthiasplappert/keras-rl/issues/161/comments","events_url":"https://api.github.com/repos/matthiasplappert/keras-rl/issues/161/events","html_url":"https://github.com/matthiasplappert/keras-rl/issues/161","id":284740810,"number":161,"title":"Feature request: np_episodes as new option to fit()","user":{"login":"AxKo","id":34890959,"avatar_url":"https://avatars0.githubusercontent.com/u/34890959?v=4","gravatar_id":"","url":"https://api.github.com/users/AxKo","html_url":"https://github.com/AxKo","followers_url":"https://api.github.com/users/AxKo/followers","following_url":"https://api.github.com/users/AxKo/following{/other_user}","gists_url":"https://api.github.com/users/AxKo/gists{/gist_id}","starred_url":"https://api.github.com/users/AxKo/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/AxKo/subscriptions","organizations_url":"https://api.github.com/users/AxKo/orgs","repos_url":"https://api.github.com/users/AxKo/repos","events_url":"https://api.github.com/users/AxKo/events{/privacy}","received_events_url":"https://api.github.com/users/AxKo/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":6,"created_at":"2017-12-27T15:11:29Z","updated_at":"2018-02-07T23:58:37Z","closed_at":null,"author_association":"NONE","body":"Hi,\r\nI recently discovered keras-rl and noticed that it is possible to specify the number of steps for fitting, but not the number of episodes. I would find that a very useful feature and it only requires to change a few lines of code in core.py. \r\nI did that in my installation, but it would be nice to have it in the official distribution. So this would be my feature request for the next release. I'm also happy to somehow provide my code changes if somebody tells me the best way to do this (I'm not so familiar with github).\r\n\r\nMany thanks,\r\n   Axel\r\n\r\n\r\n"},"comment":{"url":"https://api.github.com/repos/matthiasplappert/keras-rl/issues/comments/363954082","html_url":"https://github.com/matthiasplappert/keras-rl/issues/161#issuecomment-363954082","issue_url":"https://api.github.com/repos/matthiasplappert/keras-rl/issues/161","id":363954082,"user":{"login":"Sat0ri","id":33501571,"avatar_url":"https://avatars0.githubusercontent.com/u/33501571?v=4","gravatar_id":"","url":"https://api.github.com/users/Sat0ri","html_url":"https://github.com/Sat0ri","followers_url":"https://api.github.com/users/Sat0ri/followers","following_url":"https://api.github.com/users/Sat0ri/following{/other_user}","gists_url":"https://api.github.com/users/Sat0ri/gists{/gist_id}","starred_url":"https://api.github.com/users/Sat0ri/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Sat0ri/subscriptions","organizations_url":"https://api.github.com/users/Sat0ri/orgs","repos_url":"https://api.github.com/users/Sat0ri/repos","events_url":"https://api.github.com/users/Sat0ri/events{/privacy}","received_events_url":"https://api.github.com/users/Sat0ri/received_events","type":"User","site_admin":false},"created_at":"2018-02-07T23:58:37Z","updated_at":"2018-02-07T23:58:37Z","author_association":"NONE","body":"Hi @pollini , for example, train the net untill it reach **best** reward after save&test it.\r\n\r\nThis code:\r\n`dqn.fit(env, nb_episodes=nb_ep, visualize=False, verbose=2)`\r\n`dqn.save_weights('Saved/weights.h5f', overwrite=True)`\r\n\r\n...sometimes when it training for **_n_** episodes it reach best reward early, but at the end of training the reward will be not the maximun, and it will save it anyway\r\n\r\nthis one: \r\n`dqn.fit(env, nb_episodes=nb_ep, **br=best_reward**, visualize=False, verbose=2)\r\ndqn.save_weights('Saved/weights.h5f', overwrite=True)`\r\nwill save weights with best reward/results.\r\n\r\n"}}
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