{"action":"reopened","issue":{"url":"https://api.github.com/repos/JuliaPOMDP/POMDPs.jl/issues/85","repository_url":"https://api.github.com/repos/JuliaPOMDP/POMDPs.jl","labels_url":"https://api.github.com/repos/JuliaPOMDP/POMDPs.jl/issues/85/labels{/name}","comments_url":"https://api.github.com/repos/JuliaPOMDP/POMDPs.jl/issues/85/comments","events_url":"https://api.github.com/repos/JuliaPOMDP/POMDPs.jl/issues/85/events","html_url":"https://github.com/JuliaPOMDP/POMDPs.jl/issues/85","id":156346293,"number":85,"title":"should there be a unified interface for vectorized representations of state?","user":{"login":"cho3","id":8715820,"avatar_url":"https://avatars3.githubusercontent.com/u/8715820?v=3","gravatar_id":"","url":"https://api.github.com/users/cho3","html_url":"https://github.com/cho3","followers_url":"https://api.github.com/users/cho3/followers","following_url":"https://api.github.com/users/cho3/following{/other_user}","gists_url":"https://api.github.com/users/cho3/gists{/gist_id}","starred_url":"https://api.github.com/users/cho3/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/cho3/subscriptions","organizations_url":"https://api.github.com/users/cho3/orgs","repos_url":"https://api.github.com/users/cho3/repos","events_url":"https://api.github.com/users/cho3/events{/privacy}","received_events_url":"https://api.github.com/users/cho3/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":10,"created_at":"2016-05-23T19:19:31Z","updated_at":"2017-05-09T20:57:21Z","closed_at":null,"body":"If you're using a function approximation (eg neural network) for your values (or directly learning a policy for instance), then you need a vectorized representation of your state/observation. \n\nIs there a need for a unified interface for this particular feature or should this be pushed off to the solvers?\n\nIf the former is true, some suggestions for what can be done:\n\n```\nvec(state)\nvec(mdp, state)\nfeature_function = generate_feature_function(mdp)\n```\n\nThe first two would be fairly easy to interpret semantically, but the last one might save some overhead (this is what I do in my ReinforcementLearning.jl package)\n\nThoughts?\n"}}
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