{"action":"created","issue":{"url":"https://api.github.com/repos/openai/gym/issues/16","repository_url":"https://api.github.com/repos/openai/gym","labels_url":"https://api.github.com/repos/openai/gym/issues/16/labels{/name}","comments_url":"https://api.github.com/repos/openai/gym/issues/16/comments","events_url":"https://api.github.com/repos/openai/gym/issues/16/events","html_url":"https://github.com/openai/gym/issues/16","id":151660112,"number":16,"title":"[working deep RL demo][need help]Lasagne+Agentnet baselines","user":{"login":"justheuristic","id":3491902,"avatar_url":"https://avatars.githubusercontent.com/u/3491902?v=3","gravatar_id":"","url":"https://api.github.com/users/justheuristic","html_url":"https://github.com/justheuristic","followers_url":"https://api.github.com/users/justheuristic/followers","following_url":"https://api.github.com/users/justheuristic/following{/other_user}","gists_url":"https://api.github.com/users/justheuristic/gists{/gist_id}","starred_url":"https://api.github.com/users/justheuristic/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/justheuristic/subscriptions","organizations_url":"https://api.github.com/users/justheuristic/orgs","repos_url":"https://api.github.com/users/justheuristic/repos","events_url":"https://api.github.com/users/justheuristic/events{/privacy}","received_events_url":"https://api.github.com/users/justheuristic/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":4,"created_at":"2016-04-28T14:42:21Z","updated_at":"2016-04-29T20:21:13Z","closed_at":null,"body":"Greetings!\r\nWe happen to have just pushed into the open source one of the Lasagne-based library for reinforcement learning algorithm design.\r\n\r\n - The repo's here: [AgentNet](https://github.com/yandexdataschool/AgentNet)\r\n - [The simple-stupid CNN demo for Atari AirRaid ](https://github.com/yandexdataschool/AgentNet/blob/master/examples/AirRaid%20-%20Simple%20ConvNet.ipynb) (current notebook output is training session that was interrupted earlier than should be)\r\n - [The same notebook that works trains weaker dense network for SpaceInvaders](https://github.com/yandexdataschool/AgentNet/blob/master/research/gym/Invaders%20-%20Simple%20Dense%20Netrork.ipynb)\r\n   - This is better if you have no GPU\r\n - [Some tinkering with SpaceInvaders](https://github.com/yandexdataschool/AgentNet/tree/master/research/gym)\r\n\r\n\r\nOn the bright side, \r\n- it is capable of sustaining virtually any custom RL (and even non-RL) architecture using minimalistic Lasagne network design \r\n- it has most of the generic reinforcement learning algorithms (Q-learning, K-step algos, SARSA, Advantage Actor-Critic)\r\n- It is also capable of interacting with any external environment with a simple wrapper.\r\n\r\nOn the gloomy one,  it has been made public ~4 days ago and doesn't have a community yet. Prior to that, it's only been used by several Yandex researchers for tinkering.\r\n\r\nI would very much like to provide a set of baseline training/testing stands for several problems (and i will do so shortly), for people to be able to experiment with NN architecture, but i'm a bit doubtful about\r\n- whether the interface of the library doesn't have any flaws worth immediate fixing\r\n- whether there is someone interested in tinkering with openai baselines, provided they're readable and performing reasonably.\r\n\r\nThe most basic Reinforcement Learning pipeline looks [like this](https://github.com/yandexdataschool/AgentNet/blob/master/examples/Basic%20tutorial%20on%20Boolearn%20Reasoning%20problem.ipynb)\r\n\r\nThe questions are, again, if there is anyone interested in having such baselines for gym problems, and if so, what are possible api improvements you would recommend?"},"comment":{"url":"https://api.github.com/repos/openai/gym/issues/comments/215870180","html_url":"https://github.com/openai/gym/issues/16#issuecomment-215870180","issue_url":"https://api.github.com/repos/openai/gym/issues/16","id":215870180,"user":{"login":"wojzaremba","id":1286529,"avatar_url":"https://avatars.githubusercontent.com/u/1286529?v=3","gravatar_id":"","url":"https://api.github.com/users/wojzaremba","html_url":"https://github.com/wojzaremba","followers_url":"https://api.github.com/users/wojzaremba/followers","following_url":"https://api.github.com/users/wojzaremba/following{/other_user}","gists_url":"https://api.github.com/users/wojzaremba/gists{/gist_id}","starred_url":"https://api.github.com/users/wojzaremba/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/wojzaremba/subscriptions","organizations_url":"https://api.github.com/users/wojzaremba/orgs","repos_url":"https://api.github.com/users/wojzaremba/repos","events_url":"https://api.github.com/users/wojzaremba/events{/privacy}","received_events_url":"https://api.github.com/users/wojzaremba/received_events","type":"User","site_admin":false},"created_at":"2016-04-29T20:21:13Z","updated_at":"2016-04-29T20:21:13Z","body":"Awesome. Looking forward for you submissions."}}