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visualize.py
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visualize.py
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# Copied from https://github.com/emansim/baselines-mansimov/blob/master/baselines/a2c/visualize_atari.py
# and https://github.com/emansim/baselines-mansimov/blob/master/baselines/a2c/load.py
# Thanks to the author and OpenAI team!
import glob
import json
import os
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import medfilt
matplotlib.rcParams.update({'font.size': 8})
def smooth_reward_curve(x, y):
# Halfwidth of our smoothing convolution
halfwidth = min(31, int(np.ceil(len(x) / 30)))
k = halfwidth
xsmoo = x[k:-k]
ysmoo = np.convolve(y, np.ones(2 * k + 1), mode='valid') / \
np.convolve(np.ones_like(y), np.ones(2 * k + 1), mode='valid')
downsample = max(int(np.floor(len(xsmoo) / 1e3)), 1)
return xsmoo[::downsample], ysmoo[::downsample]
def fix_point(x, y, interval):
np.insert(x, 0, 0)
np.insert(y, 0, 0)
fx, fy = [], []
pointer = 0
ninterval = int(max(x) / interval + 1)
for i in range(ninterval):
tmpx = interval * i
while pointer + 1 < len(x) and tmpx > x[pointer + 1]:
pointer += 1
if pointer + 1 < len(x):
alpha = (y[pointer + 1] - y[pointer]) / \
(x[pointer + 1] - x[pointer])
tmpy = y[pointer] + alpha * (tmpx - x[pointer])
fx.append(tmpx)
fy.append(tmpy)
return fx, fy
def load_data(indir, smooth, bin_size):
datas = []
infiles = glob.glob(os.path.join(indir, '*.monitor.csv'))
for inf in infiles:
with open(inf, 'r') as f:
f.readline()
f.readline()
for line in f:
tmp = line.split(',')
t_time = float(tmp[2])
tmp = [t_time, int(tmp[1]), float(tmp[0])]
datas.append(tmp)
datas = sorted(datas, key=lambda d_entry: d_entry[0])
result = []
timesteps = 0
for i in range(len(datas)):
result.append([timesteps, datas[i][-1]])
timesteps += datas[i][1]
if len(result) < bin_size:
return [None, None]
x, y = np.array(result)[:, 0], np.array(result)[:, 1]
if smooth == 1:
x, y = smooth_reward_curve(x, y)
if smooth == 2:
y = medfilt(y, kernel_size=9)
x, y = fix_point(x, y, bin_size)
return [x, y]
color_defaults = [
'#1f77b4', # muted blue
'#ff7f0e', # safety orange
'#2ca02c', # cooked asparagus green
'#d62728', # brick red
'#9467bd', # muted purple
'#8c564b', # chestnut brown
'#e377c2', # raspberry yogurt pink
'#7f7f7f', # middle gray
'#bcbd22', # curry yellow-green
'#17becf' # blue-teal
]
def visdom_plot(viz, win, folder, game, name, bin_size=100, smooth=1):
tx, ty = load_data(folder, smooth, bin_size)
if tx is None or ty is None:
return win
fig = plt.figure()
plt.plot(tx, ty, label="{}".format(name))
# Ugly hack to detect atari
if game.find('NoFrameskip') > -1:
plt.xticks([1e6, 2e6, 4e6, 6e6, 8e6, 10e6],
["1M", "2M", "4M", "6M", "8M", "10M"])
plt.xlim(0, 10e6)
else:
plt.xticks([1e5, 2e5, 4e5, 6e5, 8e5, 1e5],
["0.1M", "0.2M", "0.4M", "0.6M", "0.8M", "1M"])
plt.xlim(0, 1e6)
plt.xlabel('Number of Timesteps')
plt.ylabel('Rewards')
plt.title(game)
plt.legend(loc=4)
plt.show()
plt.draw()
image = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
image = image.reshape(fig.canvas.get_width_height()[::-1] + (3, ))
plt.close(fig)
# Show it in visdom
image = np.transpose(image, (2, 0, 1))
return viz.image(image, win=win)
if __name__ == "__main__":
from visdom import Visdom
viz = Visdom()
visdom_plot(viz, None, '/tmp/gym/', 'BreakOut', 'a2c', bin_size=100, smooth=1)