{"action":"created","comment":{"url":"https://api.github.com/repos/sotetsuk/algo-rl-ja/pulls/comments/58697422","id":58697422,"diff_hunk":"@@ -24,159 +24,174 @@ \\section{Overview}\n \\end{figure}\n \n % In this \\article,  we assume that the system that we wish to control is stochastic.\n-本書では，我々が制御したいシステムは確率的に与えられることを仮定する．\n+本書では，我々が制御したいシステムは確率的であることを仮定する．\n % Further, we assume that the measurements available on the system's state are detailed enough so that the the controller can avoid reasoning about how to collect information about the state.\n-さらに，システムの状態を評価する指標は十分に明らかで，制御器がシステムの状態をどのように把握するかについては考慮しなくてもよいものとする．\n+さらに，システムの状態を評価に使える指標は十分に詳細に記述されており，制御器がシステムの状態をどのように把握するかについては考慮しなくてもよいものとする．\n % Problems with these characteristics are best described in the framework of Markovian Decision Processes (MDPs).\n-これらの特徴を持つ問題には，マルコフ決定過程(MDP)の枠組みが最も良い説明を与える．\n+これらの特徴を持つ問題は，マルコフ決定過程(Markovian Decision Process; MDP)の枠組みが記述に最適である．\n % The standard approach to `solve' MDPs is to use dynamic programming, which transforms the problem of finding a good controller into the problem of finding a good value function.\n-MDPを解くための標準的な手法は動的計画法を用いることで，動的計画法は良い制御器を見つける問題を良い価値関数を見つける問題に落とし込んで扱う．\n+MDPを「解く」ための標準的アプローチは動的計画法を用いることである．\n+動的計画法は，良い制御器を見つける問題を，良い価値関数を見つける問題に落とし込む．\n % However, apart from the simplest cases when the MDP has very few states and actions, dynamic programming is infeasible.\n+しかしながら，MDPが極めて少ない状態数と行動数しか持たない最も単純な場合を除いて，動的計画法は実行不可能である．\n % The RL algorithms that we  discuss here can be thought of as a way of turning the infeasible dynamic programming methods into practical algorithms so that they can be applied to  large-scale problems.\n ここで我々が議論する強化学習のアルゴリズムは，実行不可能な動的計画法を現実的なアルゴリズムに落とし込み，大規模な問題に対して適用可能にする方法だと考えられる．\n \n % There are two key ideas that allow RL algorithms to achieve this goal.\n-強化学習がこの目標を達成するために重要なアイデアが二つある．\n+強化学習がこの目標を達成するために重要となるアイデアが二つある．\n % The first idea is to use samples to compactly represent the dynamics of the control problem.\n-一つは，制御問題のダイナミクスを簡潔に表現するためにサンプリングを行うことだ．\n-% This is important for two reasons: First, it allows one to deal with learning scenarios when the dynamics is unknown.\n-これは二つの理由で重要だ．一つは実際のダイナミクスが明らかでない場合も，学習のシナリオについて扱うことが可能になるためである．\n+一つ目は，制御問題のダイナミクスを簡潔に表現するためにサンプリングを行うことだ．\n+% This is important for two reasons:\n+これは二つの理由で重要だ．\n+% First, it allows one to deal with learning scenarios when the dynamics is unknown.\n+まず，実際のダイナミクスが未知である場合でも，学習のシナリオについて扱うことが可能になるためである．\n % Second, even if the dynamics is available, exact reasoning that uses it might be intractable on its own.\n-二つ目は，仮にダイナミクスが明らかな場合でも，システムを扱うのが困難な明確な理由が存在する場合があるためだ．\n+次に，仮にダイナミクスが明らかな場合でも，システムを扱うのが困難な明確な理由が存在する場合があるためだ．\n % The second key idea behind RL algorithms is to use powerful function approximation methods to compactly represent value functions.\n-重要なアイデアの二つ目は，価値観数を簡潔に表現するために強力な関数近似法を用いることだ．\n+重要なアイデアの二つ目は，価値関数を簡潔に表現するために強力な関数近似法を用いることだ．\n % The significance of this is that it allows dealing with large, high-dimensional state- and action-spaces.\n-このアイデアは大規模で高次元な状態や行動空間を扱うことを可能にするために重要である．\n-% What is more, the two ideas fit nicely together: Samples may be focused on a small subset of the spaces they belong to, which clever function approximation techniques might exploit.\n-都合の良いことに，この二つのアイデアはうまく協調することができて，サンプリングが全空間のうちのある部分空間に焦点を当てるようになると，より賢い関数近似法が採用できるようになる．\n+このアイデアは大規模で高次元な状態，行動空間を扱うことを可能にするために重要である．\n+% What is more, the two ideas fit nicely together:\n+都合の良いことに，この二つのアイデアはうまく協調することができる:\n+% Samples may be focused on a small subset of the spaces they belong to, which clever function approximation techniques might exploit.\n+サンプルが空間のうちのある小さな領域に集中を当てるようになると，より適した関数近似法を利用できるようになるかもしれない．\n % It is the understanding of the interplay between dynamic programming, samples and function approximation that is at the heart of designing, analyzing and applying  RL algorithms.\n-つまり動的計画法とサンプリングと関数近似法を相互に活かすことが強化学習の設計，分析，適用の核であると理解できるだろう．\n+動的計画法と，サンプリングと関数近似法の相互作用を理解することこそが，強化学習アルゴリズムの設計，分析及び適用の本質である．\n \n % The purpose of this \\article\\ is to allow the reader to have a chance to peek into this beautiful field.\n 本書の目的は読者に対してこの美しい領域を垣間見る機会を提供することである\n % However, certainly we are not the first to set out to accomplish this goal.\n-しかし，こうした取り組みを目指したのは我々が初めてではない．　\n+しかし，この目的の達成を目指したのはもちろん我々が初めてではない．\n % In \\citeyear{KaeLiMo96}, \\citeauthor{KaeLiMo96} have written a nice, compact survey about the approaches and algorithms available at the time \\citep{KaeLiMo96}.\n-\\citeyear{KaeLiMo96}年には\\citeauthor{KaeLiMo96}がその当時知られていたアプローチとアルゴリズムについて，素晴らしい簡潔なサーベイを書いている\\citep{KaeLiMo96}．\n+\\citeyear{KaeLiMo96}年には\\citeauthor{KaeLiMo96}がその当時知られていたアプローチとアルゴリズムについて，素晴らしい簡潔なサーベイを書いた\\citep{KaeLiMo96}．\n % This was followed by the publication of the book by \\citet{berttsit96}, which detailed the theoretical foundations.\n その後，\\citet{berttsit96}の著書で理論的基礎について詳細に記述した本が出版された．\n-% A few years later \\citeauthor{SuBa98}, the fathers' of RL, published their book, where they presented their ideas on RL in a very clear and accessible manner \\citep{SuBa98}.\n-\"強化学習の父\"である\\citeauthor{SuBa98}が彼らの著書を出版した数年後，彼らのアイデアをとても明快かつ分かりやすく表現した著書を出版した\\citep{SuBa98}．\n+% A few years later \\citeauthor{SuBa98}, the `fathers' of RL, published their book, where they presented their ideas on RL in a very clear and accessible manner \\citep{SuBa98}.\n+数年後，強化学習の「父」である\\citeauthor{SuBa98}が著書を出版し，彼らの強化学習におけるアイデアを極めて明快かつ分かりやすく提供した\\citep{SuBa98}．\n % A more recent and  comprehensive overview of the tools and techniques of dynamic programming/optimal control is given in the two-volume book by \\citet{Ber07:DPbookVol1,Ber07:DPbookVol2} which devotes one chapter to RL methods.\n-さらに，包括的かつ直近の動的計画法や最適制御に関する概説は\\citet{Ber07:DPbookVol1,Ber07:DPbookVol2}の上下巻の著書に載っており，一章分を強化学習のために充てている．\n+より新しく包括的な動的計画法/最適制御のツールやテクニックに関する概説は\\citet{Ber07:DPbookVol1,Ber07:DPbookVol2}の上下巻の著書で与えられており，一章分を強化学習の手法に充てている．\n % \\myfootnote{In this book, RL is called neuro-dynamic programming or approximate dynamic programming.\n % The term neuro-dynamic programming stems from the fact that, in many cases, RL algorithms are used with artificial neural networks.}\n+\\myfootnote{この本では，強化学習はニューロ動的計画法または近似動的計画法と呼ばれている．\n+ニューロ動的計画法という単語は，強化学習アルゴリズムは多くの場合，人工ニューラルネットワークが使われるという事実が由来である．\n+}\n % At times, when a field is rapidly developing, books can get out of date pretty quickly.\n-分野が急速に発展する状況では，著書は一瞬で時代遅れになってしまう．\n+時に，分野が急速に発展する状況では，著書は一瞬で時代遅れになってしまう．\n % In fact, to keep up with the growing body of new results, Bertsekas maintains an online version of his Chapter 6 of Volume II of his book, which, at the time of writing this survey counted as much as 160 pages \\citep{Ber10:DPbookVol2Ch6}.\n-実際，増え続ける最新の結果に対応するために，Bertsekasは著書\\citep{Ber10:DPbookVol2Ch6}のオンライン版において第2巻の第6章の整備を続けており，本書の執筆時点で160ページにも及ぶ．\n+実際，増え続ける新しい結果に対応するために，\\citeauthor{Ber10:DPbookVol2Ch6}は著書\\citep{Ber10:DPbookVol2Ch6}のオンライン版において第2巻の第6章の整備を続けており，本書の執筆時点で160ページにも及ぶ．\n % Other recent books on the subject include the book of \\cite{Gosavi03} who devotes 60 pages to reinforcement learning algorithms in Chapter 9, concentrating on average cost problems, or that of \\citet{Cao07} who focuses on policy gradient methods.\n-その他の近年の著書では，第9章の60ページを強化学習に充て，主に平均コスト問題に注目した\\cite{Gosavi03}や方策勾配法に焦点を当てた\\citet{Cao07}などが挙げられる．\n+近年の関連する他の著書として，第9章の60ページを強化学習に充て平均コスト問題に集中した\\cite{Gosavi03}や，方策勾配法に焦点を当てた\\citet{Cao07}などが挙げられる．\n % \\citet{Powell07} presents the algorithms and ideas from an operations research perspective and emphasizes methods that are capable of handling large control spaces, \\citet{ChaFuHuMa08} focuses on adaptive sampling (i.e., simulation-based performance optimization), while the center of the recent book by \\citet{BuBaSchEr10:RLFapp} is function approximation.\n-\\citet{Powell07}ではオペレーションズ・リサーチの視点から得られたアイデアとアルゴリズムについて言及している．また，特に大規模な制御空間を扱うことのできる手法は，\\citet{ChaFuHuMa08}が適応的サンプリング（つまり，シミュレーションベースの性能最適化）に焦点を当てた著書を出版している．一方で，\\citet{BuBaSchEr10:RLFapp}は関数近似法を中心に据えた著書を出版している．\n+\\citet{Powell07}はオペレーションズ・リサーチの視点からアルゴリズムとアイディアについて言及し，大規模な制御空間を扱うことのできる手法を強調し，\n+\\citet{ChaFuHuMa08}は適応的サンプリング（つまり，シミュレーションベースの性能最適化）に焦点を当てている．\n+一方で，近年出版された\\citet{BuBaSchEr10:RLFapp}は関数近似法を中心に据えている．\n \n % Thus, by no means do RL researchers lack a good body of literature.\n-以上より強化学習の良い書籍がない訳では全くない．\n+以上から分かるように，強化学習の研究者が良い書籍に事欠いている訳では決してない．\n % However, what seems to be missing is a self-contained and yet relatively short summary that can help newcomers to the field to develop a good sense of the state of the art, as well as existing researchers to broaden their overview of the field, an article, similar to that of \\citet{KaeLiMo96}, but with an updated contents.\n-しかし，自己完結しつつ，この分野の初学者が分野の最先端の感覚を掴むことを目的とした，比較的短い概説はまだないように思われる．\n-論文としては似たものに\\citet{KaeLiMo96}が挙げられるが，最近更新されたコンテンツではない．\n+しかしながらまだ存在しないと思われるのは，自己完結しつつも比較的短く，既存の研究者が分野の概観を広げられるだけでなく，\n+初学者が最先端の感覚を養うのに資するサマリである．\n+それは，論文としては\\citet{KaeLiMo96}に近く，更新されたコンテンツを備えたものである．\n % To fill this gap is the very purpose of this short \\article.\n この間を埋めることがまさしく本書の目的である．\n \n % Having the goal of keeping the text short, we had to make a few, hopefully, not too troubling compromises.\n-文章全体を短くするという目的を持っているため，おそらく厄介でない範囲で，いくつか妥協しているポイントもある．\n+紙面を短くするため，いくらか（願わくばさほど問題ない範囲で）妥協している点もある．\n % The first compromise we made was to present results only for the total expected discounted reward criterion.\n-一つ目の妥協点として我々が決めたことは，割引報酬の総和の期待値による基準の結果のみについて説明をおこなうことである．\n+一つ目の妥協点は，累積割引報酬和の期待値による基準についての結果のみについて説明を行うことである．\n % This choice is motivated by that this is the criterion that is both widely used and the easiest to deal with mathematically.\n-これを選択したモチベーションとしては，広くこの基準が使われていることと数学的に扱いやすいことが挙げられる．\n+これを選択したモチベーションは，広くこの基準が使われていることと数学的な扱いが最も容易であることである．\n % The next compromise is that the background on MDPs and dynamic programming is kept ultra-compact (although an appendix is added that explains these basic results).\n-次の妥協点としては，MDPと動的計画法の背景をかなり簡潔にまとめたことだ．（一応，この分野の基礎的な研究を説明するためのappendixを用意してはいる．）\n+次の妥協点は，MDPと動的計画法の背景をかなり簡潔にまとめたことだ（これらの基本的な結果を説明するappendixを用意してはいるが）．\n % Apart from these, the \\article\\ aims to cover a bit of all aspects of RL, up to the level that the reader should be able to understand the whats and hows, as well as to implement the algorithms presented.\n-これらとは別に，本書は読者が何がどのように動いているのか理解して，ここで説明されているアルゴリズムを実装できるレベルに達するように，強化学習の全ての側面について少しずつカバーすることを目的とする．\n+これらとは別に本書の目的は，読者が示されたアルゴリズムを実装出来るだけでなく，何がどのようになっているのかを理解することが出来るレベルまで到れるよう，強化学習のあらゆる側面の一塊を網羅することである．\n % Naturally, we still had to be selective in what we present.\n-しかし，当然ながら，我々は何について重点的に説明するかを選択する必要がある．\n+当然ながら何について重点的に説明するかを選択する必要がある．\n % Here, the decision was to focus on the basic algorithms,  ideas, as well as the available theory.\n-そこで，我々は有用な理論だけでなく，基本的なアルゴリズムやアイデアも同じくらい重視することとした．\n+そこで，我々は実用的な理論だけでなく，基本的なアルゴリズムやアイデアも同様に重視することとした．\n % Special attention was paid to describing the choices of the user, as well as the tradeoffs that come with these.\n-読者に対して複数の選択肢を提示するだけでなく，選択に起因するトレードオフについても理解してもらうことに注意を払った．\n+読者に対して複数の選択肢を提示するだけでなく，選択に起因するトレードオフについても理解してもらうことに特別注意を払った．\n % We tried to be impartial as much as possible, but some personal bias, as usual, surely remained.\n-我々は可能な限り公平な記述を心がけたが，いくらか個人的なバイアスが乗ってしまっていることは否めない．\n+我々は可能な限り公平な記述を心がけたが，個人的ないくらかバイアスが乗ってしまっていることには留意されたい．","path":"tex/100_overview.tex","position":156,"original_position":156,"commit_id":"92975e2f6e8ed90a00ab088146eed203d2e75e9c","original_commit_id":"92975e2f6e8ed90a00ab088146eed203d2e75e9c","user":{"login":"sotetsuk","id":5868442,"avatar_url":"https://avatars.githubusercontent.com/u/5868442?v=3","gravatar_id":"","url":"https://api.github.com/users/sotetsuk","html_url":"https://github.com/sotetsuk","followers_url":"https://api.github.com/users/sotetsuk/followers","following_url":"https://api.github.com/users/sotetsuk/following{/other_user}","gists_url":"https://api.github.com/users/sotetsuk/gists{/gist_id}","starred_url":"https://api.github.com/users/sotetsuk/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/sotetsuk/subscriptions","organizations_url":"https://api.github.com/users/sotetsuk/orgs","repos_url":"https://api.github.com/users/sotetsuk/repos","events_url":"https://api.github.com/users/sotetsuk/events{/privacy}","received_events_url":"https://api.github.com/users/sotetsuk/received_events","type":"User","site_admin":false},"body":"いくらかの","created_at":"2016-04-06T12:44:05Z","updated_at":"2016-04-06T12:44:05Z","html_url":"https://github.com/sotetsuk/algo-rl-ja/pull/24#discussion_r58697422","pull_request_url":"https://api.github.com/repos/sotetsuk/algo-rl-ja/pulls/24","_links":{"self":{"href":"https://api.github.com/repos/sotetsuk/algo-rl-ja/pulls/comments/58697422"},"html":{"href":"https://github.com/sotetsuk/algo-rl-ja/pull/24#discussion_r58697422"},"pull_request":{"href":"https://api.github.com/repos/sotetsuk/algo-rl-ja/pulls/24"}}},"pull_request":{"url":"https://api.github.com/repos/sotetsuk/algo-rl-ja/pulls/24","id":65412009,"html_url":"https://github.com/sotetsuk/algo-rl-ja/pull/24","diff_url":"https://github.com/sotetsuk/algo-rl-ja/pull/24.diff","patch_url":"https://github.com/sotetsuk/algo-rl-ja/pull/24.patch","issue_url":"https://api.github.com/repos/sotetsuk/algo-rl-ja/issues/24","number":24,"state":"open","locked":false,"title":"@sotetsuk による校閲・修正","user":{"login":"sotetsuk","id":5868442,"avatar_url":"https://avatars.githubusercontent.com/u/5868442?v=3","gravatar_id":"","url":"https://api.github.com/users/sotetsuk","html_url":"https://github.com/sotetsuk","followers_url":"https://api.github.com/users/sotetsuk/followers","following_url":"https://api.github.com/users/sotetsuk/following{/other_user}","gists_url":"https://api.github.com/users/sotetsuk/gists{/gist_id}","starred_url":"https://api.github.com/users/sotetsuk/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/sotetsuk/subscriptions","organizations_url":"https://api.github.com/users/sotetsuk/orgs","repos_url":"https://api.github.com/users/sotetsuk/repos","events_url":"https://api.github.com/users/sotetsuk/events{/privacy}","received_events_url":"https://api.github.com/users/sotetsuk/received_events","type":"User","site_admin":false},"body":"#2 \r\n\r\n# 気になる点\r\n\r\n- [ ] [performance measure](https://github.com/sotetsuk/algo-rl-ja/pull/24/files#r58653084)\r\n- [ ] refer to ... \r\n- [ ] may … which might\r\n- [ ] It is the understanding of the interplay between dynamic programming, samples and function approximation that is at the heart of designing, analyzing and applying  RL algorithms.\r\n- [ ] that は the understanding …. at the heart of designing にかかる\r\n- [ ] a bit of all aspects\r\n- [ ] Special attention was paid to describing the choices of the user, as well as the tradeoffs that come with these.\r\n- [ ] advanced undergraduates\r\n- [ ] one value per state 価値？値？ \r\n- [ ] value iteration from dynamic programming \r\n- [ ] These can be viewed as the learning analogue of value iteration of dynamic 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