mirror of
https://github.com/wassname/pytorch-a2c-ppo-acktr.git
synced 2026-06-26 16:10:05 +08:00
Ilya Kostrikov
142 lines
3.8 KiB
Python
142 lines
3.8 KiB
Python
# Copied from https://github.com/emansim/baselines-mansimov/blob/master/baselines/a2c/visualize_atari.py
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# and https://github.com/emansim/baselines-mansimov/blob/master/baselines/a2c/load.py
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# Thanks to the author and OpenAI team!
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import glob
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import json
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import os
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import numpy as np
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from scipy.signal import medfilt
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matplotlib.rcParams.update({'font.size': 8})
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def smooth_reward_curve(x, y):
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# Halfwidth of our smoothing convolution
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halfwidth = min(31, int(np.ceil(len(x) / 30)))
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k = halfwidth
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xsmoo = x[k:-k]
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ysmoo = np.convolve(y, np.ones(2 * k + 1), mode='valid') / \
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np.convolve(np.ones_like(y), np.ones(2 * k + 1), mode='valid')
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downsample = max(int(np.floor(len(xsmoo) / 1e3)), 1)
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return xsmoo[::downsample], ysmoo[::downsample]
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def fix_point(x, y, interval):
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np.insert(x, 0, 0)
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np.insert(y, 0, 0)
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fx, fy = [], []
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pointer = 0
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ninterval = int(max(x) / interval + 1)
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for i in range(ninterval):
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tmpx = interval * i
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while pointer + 1 < len(x) and tmpx > x[pointer + 1]:
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pointer += 1
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if pointer + 1 < len(x):
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alpha = (y[pointer + 1] - y[pointer]) / \
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(x[pointer + 1] - x[pointer])
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tmpy = y[pointer] + alpha * (tmpx - x[pointer])
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fx.append(tmpx)
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fy.append(tmpy)
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return fx, fy
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def load_data(indir, smooth, bin_size):
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datas = []
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infiles = glob.glob(os.path.join(indir, '*monitor.json'))
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for inf in infiles:
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with open(inf, 'r') as f:
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t_start = float(json.loads(f.readline())['t_start'])
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for line in f:
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tmp = json.loads(line)
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t_time = float(tmp['t']) + t_start
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tmp = [t_time, int(tmp['l']), float(tmp['r'])]
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datas.append(tmp)
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datas = sorted(datas, key=lambda d_entry: d_entry[0])
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result = []
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timesteps = 0
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for i in range(len(datas)):
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result.append([timesteps, datas[i][-1]])
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timesteps += datas[i][1]
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if len(result) < bin_size:
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return [None, None]
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x, y = np.array(result)[:, 0], np.array(result)[:, 1]
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if smooth == 1:
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x, y = smooth_reward_curve(x, y)
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if smooth == 2:
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y = medfilt(y, kernel_size=9)
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x, y = fix_point(x, y, bin_size)
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return [x, y]
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color_defaults = [
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'#1f77b4', # muted blue
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'#ff7f0e', # safety orange
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'#2ca02c', # cooked asparagus green
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'#d62728', # brick red
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'#9467bd', # muted purple
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'#8c564b', # chestnut brown
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'#e377c2', # raspberry yogurt pink
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'#7f7f7f', # middle gray
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'#bcbd22', # curry yellow-green
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'#17becf' # blue-teal
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]
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def visdom_plot(viz, win, folder, game, name, bin_size=100, smooth=1):
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tx, ty = load_data(folder, smooth, bin_size)
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if tx is None or ty is None:
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return win
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fig = plt.figure()
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plt.plot(tx, ty, label="{}".format(name))
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# Ugly hack to detect atari
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if game.find('NoFrameskip') > -1:
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plt.xticks([4*1e6, 4*2e6, 4*4e6, 4*6e6, 4*8e6, 4*10e6],
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["1M", "2M", "4M", "6M", "8M", "10M"])
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plt.xlim(0, 40e6)
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else:
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plt.xticks([1e5, 2e5, 4e5, 6e5, 8e5, 1e5],
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["0.1M", "0.2M", "0.4M", "0.6M", "0.8M", "1M"])
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plt.xlim(0, 1e6)
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plt.xlabel('Number of Timesteps')
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plt.ylabel('Rewards')
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plt.title(game)
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plt.legend(loc=4)
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plt.show()
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plt.draw()
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image = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
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image = image.reshape(fig.canvas.get_width_height()[::-1] + (3, ))
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plt.close(fig)
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# Show it in visdom
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image = np.transpose(image, (2, 0, 1))
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return viz.image(image, win=win)
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if __name__ == "__main__":
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from visdom import Visdom
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viz = Visdom()
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visdom_plot(viz, None, '/tmp/gym/', 'BreakOut', 'a2c', bin_size=100, smooth=1)
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