{"action":"created","issue":{"url":"https://api.github.com/repos/hill-a/stable-baselines/issues/56","repository_url":"https://api.github.com/repos/hill-a/stable-baselines","labels_url":"https://api.github.com/repos/hill-a/stable-baselines/issues/56/labels{/name}","comments_url":"https://api.github.com/repos/hill-a/stable-baselines/issues/56/comments","events_url":"https://api.github.com/repos/hill-a/stable-baselines/issues/56/events","html_url":"https://github.com/hill-a/stable-baselines/issues/56","id":367578990,"node_id":"MDU6SXNzdWUzNjc1Nzg5OTA=","number":56,"title":"Tensorboard: Continue training curves ","user":{"login":"pathway","id":18753,"node_id":"MDQ6VXNlcjE4NzUz","avatar_url":"https://avatars1.githubusercontent.com/u/18753?v=4","gravatar_id":"","url":"https://api.github.com/users/pathway","html_url":"https://github.com/pathway","followers_url":"https://api.github.com/users/pathway/followers","following_url":"https://api.github.com/users/pathway/following{/other_user}","gists_url":"https://api.github.com/users/pathway/gists{/gist_id}","starred_url":"https://api.github.com/users/pathway/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/pathway/subscriptions","organizations_url":"https://api.github.com/users/pathway/orgs","repos_url":"https://api.github.com/users/pathway/repos","events_url":"https://api.github.com/users/pathway/events{/privacy}","received_events_url":"https://api.github.com/users/pathway/received_events","type":"User","site_admin":false},"labels":[{"id":981636972,"node_id":"MDU6TGFiZWw5ODE2MzY5NzI=","url":"https://api.github.com/repos/hill-a/stable-baselines/labels/enhancement","name":"enhancement","color":"a2eeef","default":true},{"id":981636988,"node_id":"MDU6TGFiZWw5ODE2MzY5ODg=","url":"https://api.github.com/repos/hill-a/stable-baselines/labels/question","name":"question","color":"d876e3","default":true}],"state":"open","locked":false,"assignee":{"login":"hill-a","id":6171343,"node_id":"MDQ6VXNlcjYxNzEzNDM=","avatar_url":"https://avatars2.githubusercontent.com/u/6171343?v=4","gravatar_id":"","url":"https://api.github.com/users/hill-a","html_url":"https://github.com/hill-a","followers_url":"https://api.github.com/users/hill-a/followers","following_url":"https://api.github.com/users/hill-a/following{/other_user}","gists_url":"https://api.github.com/users/hill-a/gists{/gist_id}","starred_url":"https://api.github.com/users/hill-a/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/hill-a/subscriptions","organizations_url":"https://api.github.com/users/hill-a/orgs","repos_url":"https://api.github.com/users/hill-a/repos","events_url":"https://api.github.com/users/hill-a/events{/privacy}","received_events_url":"https://api.github.com/users/hill-a/received_events","type":"User","site_admin":false},"assignees":[{"login":"hill-a","id":6171343,"node_id":"MDQ6VXNlcjYxNzEzNDM=","avatar_url":"https://avatars2.githubusercontent.com/u/6171343?v=4","gravatar_id":"","url":"https://api.github.com/users/hill-a","html_url":"https://github.com/hill-a","followers_url":"https://api.github.com/users/hill-a/followers","following_url":"https://api.github.com/users/hill-a/following{/other_user}","gists_url":"https://api.github.com/users/hill-a/gists{/gist_id}","starred_url":"https://api.github.com/users/hill-a/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/hill-a/subscriptions","organizations_url":"https://api.github.com/users/hill-a/orgs","repos_url":"https://api.github.com/users/hill-a/repos","events_url":"https://api.github.com/users/hill-a/events{/privacy}","received_events_url":"https://api.github.com/users/hill-a/received_events","type":"User","site_admin":false}],"milestone":null,"comments":4,"created_at":"2018-10-07T18:13:33Z","updated_at":"2018-11-19T22:07:50Z","closed_at":null,"author_association":"NONE","body":"Currently each time we .learn() it starts a new curve on tensorboard.  This makes continuing training (in a loop, or reloading later) difficult to visualize.\r\n\r\nI was able to change logic in TensorboardWriter (_get_latest_run_id) to avoid starting a new curve with numbered postfix.\r\n\r\nHowever the global_step is still reset each time, resulting in jumbled curves. \r\n![image](https://user-images.githubusercontent.com/18753/46585147-c6a16e80-ca21-11e8-9903-367d3eeae58f.png)\r\n\r\nI would like to avoid starting the timeline from zero.  It appears acktr is the only agent type that mentions global_step.  Is that the solution for other agent types?"},"comment":{"url":"https://api.github.com/repos/hill-a/stable-baselines/issues/comments/440059236","html_url":"https://github.com/hill-a/stable-baselines/issues/56#issuecomment-440059236","issue_url":"https://api.github.com/repos/hill-a/stable-baselines/issues/56","id":440059236,"node_id":"MDEyOklzc3VlQ29tbWVudDQ0MDA1OTIzNg==","user":{"login":"bertram1isu","id":40435887,"node_id":"MDQ6VXNlcjQwNDM1ODg3","avatar_url":"https://avatars2.githubusercontent.com/u/40435887?v=4","gravatar_id":"","url":"https://api.github.com/users/bertram1isu","html_url":"https://github.com/bertram1isu","followers_url":"https://api.github.com/users/bertram1isu/followers","following_url":"https://api.github.com/users/bertram1isu/following{/other_user}","gists_url":"https://api.github.com/users/bertram1isu/gists{/gist_id}","starred_url":"https://api.github.com/users/bertram1isu/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/bertram1isu/subscriptions","organizations_url":"https://api.github.com/users/bertram1isu/orgs","repos_url":"https://api.github.com/users/bertram1isu/repos","events_url":"https://api.github.com/users/bertram1isu/events{/privacy}","received_events_url":"https://api.github.com/users/bertram1isu/received_events","type":"User","site_admin":false},"created_at":"2018-11-19T22:07:50Z","updated_at":"2018-11-19T22:07:50Z","author_association":"NONE","body":"The other thing I'm after is a consistent way to do checkpointing...   if I fix this by making the local variables within train part of the class instance variables so that they retain state across training calls, I'm also going to be looking for any other necessary variables so that when the save/load functions are called, I would be able to pick up training where I left off in the spirit of tensorflow checkpointing.  \r\n\r\nWould you prefer these are addressed in two separate issues?\r\n\r\nAlso, whatever changes I made I was looking primarily at DDPG...  the way the train functions are set up, they seem to be per algorithm...  this would mean I'd need to make the same change in each algorithm.  Seems like an indicator that there's probably a smarter way.  Has anyone given this some thought and have a better suggestion on where to make these changes?"}}
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