From c35b35130374a9f28f311277e842cf19654c97b6 Mon Sep 17 00:00:00 2001 From: starcolon Date: Wed, 30 Mar 2016 21:20:51 +0700 Subject: [PATCH] {README} Rewrite + add photos --- README.md | 84 ++++++++++++++++++++--------- media/falling-stones-benchmark.png | Bin 0 -> 100738 bytes media/ss-falling-stones.png | Bin 0 -> 96049 bytes media/ss-tictactoe.png | Bin 0 -> 160283 bytes sample/falling-stones.js | 6 +-- 5 files changed, 63 insertions(+), 27 deletions(-) create mode 100644 media/falling-stones-benchmark.png create mode 100644 media/ss-falling-stones.png create mode 100644 media/ss-tictactoe.png diff --git a/README.md b/README.md index fa10749..5c91a7a 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,10 @@ # Q-EXP [Reinforcement Learning](http://www.cs.indiana.edu/~gasser/Salsa/rl.html) -library for Node.js app. This implements Q-learning -technique for exploration-exploitation mechanic. +with Q-learning technique for Node.js app. +It also provides policy generalisation in-built. + +--- ## Installation @@ -10,9 +12,11 @@ technique for exploration-exploitation mechanic. $ npm install q-exp ``` +--- + ## Usage -To include `q-exp` library to your **Node.js** app: +>To include `q-exp` library to your **Node.js** app: ```javascript var qexp = require('q-exp'); @@ -20,23 +24,21 @@ var qexp = require('q-exp'); Read the instructions all the way down to learn how to use. -## Implementation +--- -This library is purely written in [ECMAScript 6](https://github.com/lukehoban/es6features) and runs -with [Node.js](https://nodejs.org/en/). The implementation paradigm -of the project tends towards functional programming in order to -keep operations as simple and highly separable as possible. +## Features included -### Entire pipeline inspired by Promise +>To make reinforcement learning works end-to-end, +we implement and include the following features. -Promise plays a great role in pipelining all sequential -and also parallel processings in this library. Every single -canonical operation of Q-EXP takes its arguments and returns -the Promise. This makes life easier for pipelining the sequential -processes or operations for readability and manageability and, -foremostly, side-effect-free paradigm. +- Q-learning +- Exploration-exploitation +- Generalisation with Gradient descent +- Sample usages (Tic-tac-toe and falling-stones) -## Sample pipeline of operations +--- + +## Usage To create an agent, load its learned policy from a physical file, then let it choose an action which it *believes* it would @@ -59,23 +61,25 @@ agent.then(ql.setState(initialState)) // Let the agent know the state ``` -## Sample +--- + +## Sample #1 - Tic tac toe + +![Tictactoe](/media/ss-tictactoe.png) A quick sample implementation is a classic [tic-tac-toe game](https://en.wikipedia.org/wiki/Tic-tac-toe), source code available at -[/sample/tictactoe.js](https://github.com/starcolon/q-exp/blob/master/sample/tictactoe.js). +[/sample/tictactoe.js](https://github.com/starcolon/q-exp/blob/master/sample/tictactoe.js). This sample does not make use of generalisation, +just a plain exploration-exploitation. -#### To play with the trained bot: +#### To play against the trained tic-tac-toe bot: ``` $ cd sample $ node tictactoe.js play ``` -By Q-learning definition, the bot doesn't know the rule of -the game. Yet, it knows which moves may probably lead to victory -and which moves may likely introduce defeat. The bot may sometimes -fail to end the game by an ultimate move because it doesn't know -the rule. And such ultimate pattern may not be learned by itself. +>After having your agent intensively trained for thousands games, +you'll eventually find out how strong your bot has become. #### To train the bot @@ -85,6 +89,38 @@ the rule. And such ultimate pattern may not be learned by itself. $ ./train-tictactoe ``` +--- + +## Sample #2 - Falling stones + +![Falling stones](/media/ss-falling-stones.png) + +Another classic game where two stones are falling from the +top edge of the screen at random position. The player are forced +to move left or right to escape from those falling stones. +If a stone fall onto the player, the game is over. + +This sample makes use of `generalisation` so it can +survive longer even you train it for just ten or twenty games. + +To run it: + +```bash + $ cd sample + $ node falling-stones.js +``` + +#### Benchmark + +After generalisation, the agent can survive slightly longer. +However we just fit the reward space with linear plane +which might not well fit critical cases. It doesn't +guarantee convergence. + +Y axis represents the number of moves it survives in a game. + +![Benchmark](/media/falling-stones-benchmark.png) + ## Licence diff --git a/media/falling-stones-benchmark.png b/media/falling-stones-benchmark.png new file mode 100644 index 0000000000000000000000000000000000000000..2730feab13f48a44794e062aa37f6ceb7862fadc GIT binary patch literal 100738 zcmeFZWmwc(*FQdtlz<>2pduv-N-GM|C`yO4G>C|FGc*DM21tX_NOyOGN=Zo9&`2}H zkVE|Tc+P#_$KgEBeg5zMFCMQ;YQC}e+H0@)tk2qSpU8=yBe+5Ufk4hlN<318Kybj1 z&}w{K@GW2Z0t^DdK4&Z{`b1Jxl+9LB0`dT9L3DHIu-Qj-taePjh7&SSaHSM#a zpXr2e0Y?spDoT_y@?3uvIKA?<;)mq;!pU_2JI)^r$^o zbQb64M^3*Q0@S(BK3_EUV z(e>Uh?<{71?kD6$QF!I>LpDXP;YOB^mI0ZtJ1Y-VnDH{*g<Gb8;O0~{?-TmdmJ3B0gCdrc?vuYbLv?N{n8>y2485;6CP)=%?!NNoXiOLk(h>`0sglYQ08asiLBk={tZV%8cax0^@b zYC7E7+&bJk-5Ru^!Tjy9GqqFfRITdrB%zWiuD#%07c3ktEeb1T7})BR<)G9 zU;BW}|GMaN|1fUl-Ey_9#B1U5bBf02mH3s&G79=0koXm)IwY7wQHRw185V_#OFahT zZcz1JK8xjZ9po_Sp3rAk^0=8JqLvokq+jxFk@@ga$#2A5g9-;RjtoRb->D)Y(_|Nq zK@rv03U92S*rz`fLn5xe@@!_XxYX5#H;!|E>-9+CXajYyge&1>-|osz-N7A4_oKeW zu`jmS^K7q5c=%)MCJ=T5LLw(w5TgJOtb}-1l_z?~mcTxowUSV`g+M57p#Nh@DqdfQ zK(OMBm7m%@m6hQ$urz1ae{T8AkloST3S13=2srYAAI%Nz^ywVU%`9yB90jlax`Ge< zjQ%snHM(Dy*qI7mdn)^cPSn!IknSG)U3SiELIiYlbOJWdU+^hD68rOZ@SEVZmv(km zd>kAw7>pgp#cpY1#BrOKmzRU{4#%B4Y~TtuTPF)UeMdG6TZTU#@~`JSGPE_YF}AWZ zwzQx_KUe>mrM;ctwQJ}X{rle^<1}>uG6OP;LoE-mtHn>#){a3yx z#*T(&PaheZ8(P?ccL?3SbDvY-*A1sW`p+x>a;y4(Zsplb9OxPRWk!F9 z>(^g_yo3k@IQ}hrA%eFhq8VU0t{Oj*R|emq=(PbWrvtv+{Np?NXXm5tSjia(L8@?v3KE{2MYMn z`1n_nZYG#PKVwsN-wb}N;QuHIzm3^TUL6}+eXJYvB4X{+T1MQbwI$0~vec?2c6;G6 zTbpqNJTn^ceDXXV6rb)DGuc?AiYIQ=&CgM=vPAjpl`J9kgt1fBt2ckcgQ_kY6trL6y_Du1(u{%_y? 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