feat(charts): draw reward charts

README.md

@@ -10,15 +10,20 @@ Here is a demonstration of the model after 3000 epochs (~5 minutes on an Intel(R
 
 ![after training](/demo/flappy-success.gif)
 
-also see: [Before training](/demo/flappy-lose.gif)
+Before training:
+
+![Before training](/demo/flappy-lose.gif)
 
 There is also a [a web version](http://labs.aylien.com/flappy-bird) available for ease of access.
 
 For each frame the bird stays alive, +0.1 score is given to him. For each wall he passes, +10 score is given.
 
+Demonstration of rewards for individuals and the mean reward over time (y axis is logarithmic):
+![reward chart](fig-log.jpg)
+
 Try it yourself
 ---------------
-You need python3 and pip for installing and running the code.
+You need python3.5 and pip for installing and running the code.
 
 First, install dependencies (you might want to create a [virtualenv](https://virtualenv.pypa.io)):
 

draw_chart.py

@@ -0,0 +1,39 @@
+import os
+import plotly.plotly as py
+import plotly.graph_objs as go
+import plotly.io as pio
+
+def draw_chart(scatter_x, scatter_y, line_x, line_y):
+    marker_opts = dict(
+        name = 'Individual Reward',
+        x = scatter_x,
+        y = scatter_y,
+        mode = 'markers',
+        marker = {
+            'size': 2
+        }
+    )
+    marker_trace = go.Scatter(**marker_opts)
+    line_opts = dict(
+        name = 'Mean Reward',
+        x = line_x,
+        y = line_y,
+        mode = 'lines'
+    )
+    line_trace = go.Scatter(**line_opts)
+
+    data = [marker_trace, line_trace]
+
+    fig = go.Figure(data=data)
+
+    log_layout = go.Layout(
+        yaxis = {
+            'type': 'log',
+            'autorange': True
+        }
+    )
+    fig_log = go.Figure(data=data, layout=log_layout)
+
+    pio.orca.config.executable = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'node_modules', 'orca', 'bin', 'orca.js')
+    pio.write_image(fig, 'fig.svg', width=1600, height=800)
+    pio.write_image(fig_log, 'fig-log.svg', width=1600, height=800)

es.py

@@ -69,5 +69,5 @@ class EvolutionStrategy():
             # np.dot(n.T, A) scales each noise's contribution to the update by how much reward it had received
             update = self.alpha / (self.noisep * self.sigma) * np.dot(n.T, A).T
             self.layers[i] = self.layers[i] + update
-            
 
+        return R

package-lock.json

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+        "extsprintf": "^1.2.0"
+      }
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+    "wrappy": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
+      "integrity": "sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=",
+      "dev": true
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+}

package.json

@@ -0,0 +1,22 @@
+{
+  "name": "flappy-es",
+  "version": "1.0.0",
+  "description": "Playing Flappy Bird using Evolution Strategies ==============================================",
+  "main": "index.js",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "repository": {
+    "type": "git",
+    "url": "git+https://github.com/mdibaiee/flappy-es.git"
+  },
+  "author": "",
+  "license": "ISC",
+  "bugs": {
+    "url": "https://github.com/mdibaiee/flappy-es/issues"
+  },
+  "homepage": "https://github.com/mdibaiee/flappy-es#readme",
+  "devDependencies": {
+    "orca": "^1.1.1"
+  }
+}

requirements

@@ -1,31 +1,4 @@
-blinker==1.3
-chardet==2.3.0
-cloud-init==0.7.9
-command-not-found==0.3
-configobj==5.0.6
-cryptography==1.2.3
-idna==2.0
-Jinja2==2.8
-jsonpatch==1.10
-jsonpointer==1.9
-language-selector==0.1
-MarkupSafe==0.23
 numpy==1.12.1
-oauthlib==1.0.3
+plotly==3.4.0
-prettytable==0.7.2
+psutil==5.4.6
-pyasn1==0.1.9
+PyGObject==3.30.4
-pycurl==7.43.0
-pygobject==3.20.0
-PyJWT==1.3.0
-pyserial==3.0.1
-python-apt==1.1.0b1
-python-debian==0.1.27
-python-systemd==231
-PyYAML==3.11
-requests==2.9.1
-six==1.10.0
-ssh-import-id==5.5
-ufw==0.35
-unattended-upgrades==0.1
-urllib3==1.13.1
-virtualenv==15.0.1

train.py

@@ -7,6 +7,7 @@ from gi.repository import Gtk, GLib, Gdk
 from datetime import datetime
 from os import path
 import os
+from draw_chart import draw_chart
 
 es = EvolutionStrategy(fn=play, noisep=50, sigma=0.1, alpha=0.001, layer_sizes=[[4, 500], [500, 1]], input_size=4)
 load = path.join(path.dirname(__file__), 'load.npy')
@@ -14,13 +15,15 @@ load = path.join(path.dirname(__file__), 'load.npy')
 np.random.seed(0)
 
 # if load.npy exists, load the parameters from it
-if path.exists(load):
+if path.exists(load) and not os.environ.get('SKIP_LOAD'):
     es.layers = np.load(load)
 
 # show the game every n iterations
-SHOW_EVERY = 100
+SHOW_EVERY = int(os.environ.get('SHOW_EVERY', 100))
 # save the parameters every n iterations
-SAVE_EVERY = 100
+SAVE_EVERY = int(os.environ.get('SAVE_EVERY', 100))
+# number of steps
+STEPS = int(os.environ.get('STEPS', 10000))
 
 # an id for saving the parameters in a folder
 run_id = str(datetime.now())
@@ -45,9 +48,22 @@ def timeout_kill(win, game):
     
     return True
 
-for i in range(10000):
+reward_scatter_x = []
+reward_scatter_y = []
+
+reward_line_x = list(range(STEPS))
+reward_line_y = []
+
+for i in range(STEPS):
     print("{}: ".format(i), end='')
-    es.train()
+    rewards = es.train()
+    m = np.mean(rewards)
+
+    reward_line_y.append(m)
+
+    for r in rewards:
+        reward_scatter_x.append(i)
+        reward_scatter_y.append(r)
 
     if SHOW_EVERY and i % SHOW_EVERY == 0:
         play(es.forward, step=step)
@@ -60,4 +76,4 @@ for i in range(10000):
     if i % SAVE_EVERY == 0:
         p = path.join(path.dirname(__file__), 'saves', run_id, 'save-{}'.format(i))
         np.save(p, es.layers)
-
+        draw_chart(reward_scatter_x, reward_scatter_y, reward_line_x, reward_line_y)