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feat(charts): draw reward charts

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This commit is contained in:
Mahdi Dibaiee 2018-12-07 10:42:26 +03:30
parent c0454e4591
commit 3d81a10bb8
8 changed files with 801 additions and 38 deletions
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2
README.md
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@ -15,7 +15,7 @@ For each frame the bird stays alive, +0.1 score is given to him. For each wall h
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)):

39
draw_chart.py Normal file
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@ -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)

2
es.py
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@ -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

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package-lock.json generated Normal file
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@ -0,0 +1,713 @@
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22
package.json Normal file
View File

@ -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"
}
}

33
requirements
View File

@ -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
prettytable==0.7.2
pyasn1==0.1.9
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
plotly==3.4.0
psutil==5.4.6
PyGObject==3.30.4

28
train.py
View File

@ -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)