payload: {"action":"created","comment":{"url":"https://api.github.com/repos/JuliaReinforcementLearning/ReinforcementLearning.jl/pulls/comments/321059653","pull_request_review_id":284000147,"id":321059653,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDMyMTA1OTY1Mw==","diff_hunk":"@@ -0,0 +1,39 @@\n+export AbstractQAgent, DQN\n+\n+abstract type AbstractQAgent <: AbstractAgent end\n+\n+selector(agent::AbstractQAgent) = agent.selector\n+\n+function (agent::AbstractQAgent)(obs::EnvObservation)\n+    obs |> state |> learner(agent) |> selector(agent)\n+end\n+\n+mutable struct DQN{Tl<:QLearner{<:NeuralNetworkQ}, Tb<:AbstractTurnBuffer, Ts<:AbstractDiscreteActionSelector} <: AbstractQAgent\n+    role::String\n+    learner::Tl\n+    buffer::Tb\n+    selector::Ts\n+    batch_size::Int\n+    update_horizon::Int  # starts with 1\n+    γ::Float64\n+    act_step::Int\n+    min_replay_history::Int\n+end","path":"src/components/agents/q_agent.jl","position":21,"original_position":21,"commit_id":"b57c09d79cc48529539fc31defa70e78587c0c89","original_commit_id":"2fadedfe67449a317cfe40dd35f10a3d19ce1f6f","user":{"login":"findmyway","id":5612003,"node_id":"MDQ6VXNlcjU2MTIwMDM=","avatar_url":"https://avatars0.githubusercontent.com/u/5612003?v=4","gravatar_id":"","url":"https://api.github.com/users/findmyway","html_url":"https://github.com/findmyway","followers_url":"https://api.github.com/users/findmyway/followers","following_url":"https://api.github.com/users/findmyway/following{/other_user}","gists_url":"https://api.github.com/users/findmyway/gists{/gist_id}","starred_url":"https://api.github.com/users/findmyway/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/findmyway/subscriptions","organizations_url":"https://api.github.com/users/findmyway/orgs","repos_url":"https://api.github.com/users/findmyway/repos","events_url":"https://api.github.com/users/findmyway/events{/privacy}","received_events_url":"https://api.github.com/users/findmyway/received_events","type":"User","site_admin":false},"body":"I have been thinking of this comment for a lot of times this week. Now I would like to retract my comment. Fortunately, it's not too late... 😃 \r\n\r\nIt's true that it would be more intuitive to move those parameters in the learner. This change will be reflected in the latest PR.\r\n\r\nThanks again for your great thoughts!","created_at":"2019-09-05T03:20:02Z","updated_at":"2019-09-05T03:20:02Z","html_url":"https://github.com/JuliaReinforcementLearning/ReinforcementLearning.jl/pull/25#discussion_r321059653","pull_request_url":"https://api.github.com/repos/JuliaReinforcementLearning/ReinforcementLearning.jl/pulls/25","author_association":"MEMBER","_links":{"self":{"href":"https://api.github.com/repos/JuliaReinforcementLearning/ReinforcementLearning.jl/pulls/comments/321059653"},"html":{"href":"https://github.com/JuliaReinforcementLearning/ReinforcementLearning.jl/pull/25#discussion_r321059653"},"pull_request":{"href":"https://api.github.com/repos/JuliaReinforcementLearning/ReinforcementLearning.jl/pulls/25"}},"in_reply_to_id":313492707},"pull_request":{"url":"https://api.github.com/repos/JuliaReinforcementLearning/ReinforcementLearning.jl/pulls/25","id":302381888,"node_id":"MDExOlB1bGxSZXF1ZXN0MzAyMzgxODg4","html_url":"https://github.com/JuliaReinforcementLearning/ReinforcementLearning.jl/pull/25","diff_url":"https://github.com/JuliaReinforcementLearning/ReinforcementLearning.jl/pull/25.diff","patch_url":"https://github.com/JuliaReinforcementLearning/ReinforcementLearning.jl/pull/25.patch","issue_url":"https://api.github.com/repos/JuliaReinforcementLearning/ReinforcementLearning.jl/issues/25","number":25,"state":"closed","locked":false,"title":"[WIP]Reorganize Existing Components","user":{"login":"findmyway","id":5612003,"node_id":"MDQ6VXNlcjU2MTIwMDM=","avatar_url":"https://avatars0.githubusercontent.com/u/5612003?v=4","gravatar_id":"","url":"https://api.github.com/users/findmyway","html_url":"https://github.com/findmyway","followers_url":"https://api.github.com/users/findmyway/followers","following_url":"https://api.github.com/users/findmyway/following{/other_user}","gists_url":"https://api.github.com/users/findmyway/gists{/gist_id}","starred_url":"https://api.github.com/users/findmyway/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/findmyway/subscriptions","organizations_url":"https://api.github.com/users/findmyway/orgs","repos_url":"https://api.github.com/users/findmyway/repos","events_url":"https://api.github.com/users/findmyway/events{/privacy}","received_events_url":"https://api.github.com/users/findmyway/received_events","type":"User","site_admin":false},"body":"Just want to let you guys know that I'm still working on improving this package.\r\n\r\nGenerally speaking, there are three major steps in my mind:\r\n\r\n1. Reorganize the code structure and keep some common components well tested.\r\n\r\n    - [x] Move some useful components from [Ju.jl](https://github.com/Ju-jl/Ju.jl/tree/master/src) here.\r\n    - [x] Move preprocessing components to [ReinforcementLearningEnvironments.jl](https://github.com/JuliaReinforcementLearning/ReinforcementLearningEnvironments.jl).\r\n\r\n1. Rewrite existing algorithms using a set of unified interfaces.\r\n    - [x] td\r\n    - [ ] mdp\r\n    - [ ] montecarlo\r\n    - [ ] prioritized sweeping\r\n    - [x] q-learning\r\n      - [x] dqn\r\n      - [ ] prioritized experience replay\r\n      - [ ] double dqn\r\n      - [ ] dueling dqn\r\n      - [ ] retrace\r\n      - [ ] noisy dqn\r\n      - [ ] distributed dqn (C51)\r\n    - [x] reinforce\r\n      - [ ] PPO\r\n      - [ ] DPPO\r\n      - [ ] TRPO\r\n    - [ ] actor critic\r\n      - [ ] a3c\r\n      - [ ] ddpg\r\n      - [ ] td3\r\n      - [ ] soft actor critic\r\n\r\n    I haven't tried to implement any model-based methods in Julia yet. So this part is left out above. In the early stage, I will only implement several algorithms above to verify that the interfaces are flexible enough. Then I'll move on to the next step.\r\n\r\n1. Leverage the multi-threading tasks to allow auto-scheduling on one node and data sharing across different nodes.\r\n\r\n    This step should be independent with the steps above. And I have a raw implementation of a global scheduler in my mind. A more practical issue is that I don't have that much machine for some experiments.\r\n\r\n 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