{"action":"closed","issue":{"url":"https://api.github.com/repos/NervanaSystems/coach/issues/120","repository_url":"https://api.github.com/repos/NervanaSystems/coach","labels_url":"https://api.github.com/repos/NervanaSystems/coach/issues/120/labels{/name}","comments_url":"https://api.github.com/repos/NervanaSystems/coach/issues/120/comments","events_url":"https://api.github.com/repos/NervanaSystems/coach/issues/120/events","html_url":"https://github.com/NervanaSystems/coach/issues/120","id":371599653,"node_id":"MDU6SXNzdWUzNzE1OTk2NTM=","number":120,"title":"Pre-Training","user":{"login":"abcp4","id":8815320,"node_id":"MDQ6VXNlcjg4MTUzMjA=","avatar_url":"https://avatars3.githubusercontent.com/u/8815320?v=4","gravatar_id":"","url":"https://api.github.com/users/abcp4","html_url":"https://github.com/abcp4","followers_url":"https://api.github.com/users/abcp4/followers","following_url":"https://api.github.com/users/abcp4/following{/other_user}","gists_url":"https://api.github.com/users/abcp4/gists{/gist_id}","starred_url":"https://api.github.com/users/abcp4/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/abcp4/subscriptions","organizations_url":"https://api.github.com/users/abcp4/orgs","repos_url":"https://api.github.com/users/abcp4/repos","events_url":"https://api.github.com/users/abcp4/events{/privacy}","received_events_url":"https://api.github.com/users/abcp4/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":2,"created_at":"2018-10-18T15:56:30Z","updated_at":"2018-12-18T13:38:31Z","closed_at":"2018-12-18T13:38:30Z","author_association":"NONE","body":"Hello, I would like to know if it is possible to first train the networks (before learning with on-policy or off-policy methods) in some dataset of experience. I know that BCAgents allow one to load a supervised dataset (state,actions), but it is only for then. I would like to know if there is some way to train the networks before any method. "}}
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