{"action":"closed","issue":{"url":"https://api.github.com/repos/jimkon/Deep-Reinforcement-Learning-in-Large-Discrete-Action-Spaces/issues/31","repository_url":"https://api.github.com/repos/jimkon/Deep-Reinforcement-Learning-in-Large-Discrete-Action-Spaces","labels_url":"https://api.github.com/repos/jimkon/Deep-Reinforcement-Learning-in-Large-Discrete-Action-Spaces/issues/31/labels{/name}","comments_url":"https://api.github.com/repos/jimkon/Deep-Reinforcement-Learning-in-Large-Discrete-Action-Spaces/issues/31/comments","events_url":"https://api.github.com/repos/jimkon/Deep-Reinforcement-Learning-in-Large-Discrete-Action-Spaces/issues/31/events","html_url":"https://github.com/jimkon/Deep-Reinforcement-Learning-in-Large-Discrete-Action-Spaces/issues/31","id":296407925,"number":31,"title":"why is this for continuous action spaces?","user":{"login":"M00NSH0T","id":30039978,"avatar_url":"https://avatars0.githubusercontent.com/u/30039978?v=4","gravatar_id":"","url":"https://api.github.com/users/M00NSH0T","html_url":"https://github.com/M00NSH0T","followers_url":"https://api.github.com/users/M00NSH0T/followers","following_url":"https://api.github.com/users/M00NSH0T/following{/other_user}","gists_url":"https://api.github.com/users/M00NSH0T/gists{/gist_id}","starred_url":"https://api.github.com/users/M00NSH0T/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/M00NSH0T/subscriptions","organizations_url":"https://api.github.com/users/M00NSH0T/orgs","repos_url":"https://api.github.com/users/M00NSH0T/repos","events_url":"https://api.github.com/users/M00NSH0T/events{/privacy}","received_events_url":"https://api.github.com/users/M00NSH0T/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":32,"created_at":"2018-02-12T15:08:51Z","updated_at":"2018-03-26T15:00:06Z","closed_at":"2018-03-26T15:00:06Z","author_association":"NONE","body":"Really cool that you've been working on implementing that algorithm in Python.  I've been thinking of doing this as well. As far as I can tell, you're the only one that's tried doing this yet, so I'm not sure if you're looking for contributors, or if I should just go work on my own. \r\n\r\nEither way, I'm just curious though, why is this implementation only for continuous action spaces? Isn't the point of the algorithm to bring the benefits of a DDPG to the large discrete space by mapping the continuous output to a set of discrete actions via the k-nearest neighbor approach?  It looks like you've started working on that, but I'm still trying to figure out your code, so sorry if I'm misunderstanding.\r\n\r\nAlso, it looks like you're building your own nearest neighbor function here.  Have you looked at using the one in sklearn? "}}
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