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example_cadrl.py
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import numpy as np
##from policies.SLSTMPolicy import SLSTMPolicy
##policy = SLSTMPolicy()
##policy.init()
from policies.CADRLPolicy import CADRLPolicy
policy = CADRLPolicy()
policy.init()
data = np.array(
[[[12.91624378, 10.83968944],
[12.83100541, 10.51081624],
[12.74787169, 10.17716984],
[12.68473216, 9.80342859],
[12.62159262, 9.42944869],
[12.55466472, 9.05857136],
[12.45364147, 8.71490125],
[12.35261821, 8.37123113]],
[[13.55100655, 10.8215513 ],
[13.46829376, 10.38671314],
[13.38558097, 9.95211365],
[13.30139493, 9.5623822 ],
[13.21720888, 9.17241208],
[13.13302284, 8.78268063],
[12.95875773, 8.42063371],
[12.78470308, 8.0585868 ]],
[[ 5.06694751, 4.39921609],
[ 5.75095912, 4.55840217],
[ 6.43497073, 4.71758826],
[ 7.09267419, 4.87080785],
[ 7.72406952, 5.01806095],
[ 8.35546485, 5.1655527 ],
[ 8.99106948, 5.29228106],
[ 9.66455783, 5.23261611]],
[[ 2.3875162 , 5.67222746],
[ 2.87095456, 5.77819241],
[ 3.31356269, 5.86840582],
[ 3.74627895, 5.954562 ],
[ 4.17899522, 6.04071819],
[ 4.60792311, 6.10825891],
[ 5.02211845, 6.1006218 ],
[ 5.43652425, 6.09322335]],
[[ 1.09631276, 8.17027958],
[ 1.39243717, 7.96908937],
[ 1.69213948, 7.81133524],
[ 2.0008918 , 7.75572751],
[ 2.30943365, 7.69988112],
[ 2.64428364, 7.65023988],
[ 3.03953711, 7.61396359],
[ 3.43500105, 7.57792596]],
[[ 0.68211742, 7.87410277],
[ 0.98371392, 7.71945122],
[ 1.31645926, 7.59630277],
[ 1.64899413, 7.47315431],
[ 1.99415691, 7.36623472],
[ 2.38877899, 7.32446925],
[ 2.78340107, 7.28270379],
[ 3.17802316, 7.24093832]],
[[14.04244258, 3.7906336 ],
[13.49081353, 3.67536092],
[12.82427052, 3.6992269 ],
[12.15793799, 3.72309288],
[11.49139499, 3.74695886],
[10.89009617, 3.70614803],
[10.28879735, 3.66509854],
[ 9.68749853, 3.62428772]],
[[ 1.17734182, 4.12881454],
[ 1.71949995, 4.12881454],
[ 2.27912667, 4.14528206],
[ 2.85032898, 4.17248928],
[ 3.42153129, 4.1996965 ],
[ 3.99946848, 4.23263155],
[ 4.63654637, 4.31640114],
[ 5.27362425, 4.40040939]],
[[ 0.93951625, 4.88918466],
[ 1.49577553, 4.85505631],
[ 2.03582901, 4.87438775],
[ 2.56914759, 4.91686919],
[ 3.10225572, 4.95935064],
[ 3.63536384, 5.00183208],
[ 4.19267545, 5.04670012],
[ 4.84553822, 5.10183054]],
[[14.51514722, 4.37535011],
[13.87659607, 4.40733052],
[13.2382554 , 4.43907227],
[12.62853797, 4.4555398 ],
[12.01903102, 4.47200733],
[11.41099731, 4.4846563 ],
[10.81790663, 4.46293825],
[10.22481596, 4.44122021]]])
agent_index = 0
#result = policy.predict(data, agent_index)
goal= np.array([11,6])
result = policy.predict(data, agent_index, goal, pref_speed= 1.0, dt=0.4) #0.2 #0.8 also tested=>ok
#plot
from matplotlib import pyplot as plt
from matplotlib import animation
import matplotlib
matplotlib.rcParams.update({'font.size': 13})
plt_colors = []
plt_colors.append([0.8500, 0.3250, 0.0980]) # orange
plt_colors.append([0.0, 0.4470, 0.7410]) # blue
plt_colors.append([0.4660, 0.6740, 0.1880]) # green
plt_colors.append([0.4940, 0.1840, 0.5560]) # purple
plt_colors.append([0.9290, 0.6940, 0.1250]) # yellow
plt_colors.append([0.3010, 0.7450, 0.9330]) # cyan
plt_colors.append([0.6350, 0.0780, 0.1840]) # chocolate
def color(agent_id, decay=None):
if decay is None:
return plt_colors[agent_id%len(plt_colors)]
else:
print(plt_colors[agent_id%len(plt_colors)])
print(decay)
color_with_alpha = plt_colors[agent_id%len(plt_colors)].copy()
color_with_alpha.append(decay)
return color_with_alpha
fig = plt.figure()
fig.set_dpi(100)
fig.set_size_inches(8, 8)
x_min = -3 #-5 #-1 17 for univ #-5 15 for biwi eth #-3 17 for zara1 #biwi_hotel -12 8
x_max = 17 #15
y_min = -3 #-5
y_max = 17 #15
plt.axis([x_min,x_max,y_min,y_max]) #for simulator
#plt.axis([0,20,0,20]) #for biwi_eth
ax = plt.gca()
# Major ticks every 20, minor ticks every 5
major_ticks = np.arange(x_min, x_max+1, 1)
minor_ticks = np.arange(x_min, x_max+1, 0.2)
ax.set_xticks(major_ticks)
ax.set_xticks(minor_ticks, minor=True)
ax.set_yticks(major_ticks)
ax.set_yticks(minor_ticks, minor=True)
ax.minorticks_on()
# Customize the major grid
ax.grid(which='major', linestyle='-', linewidth='0.5', color='red' , alpha=0.2)
# Customize the minor grid
ax.grid(which='minor', linestyle=':', linewidth='0.5', color='black' , alpha=0.2)
name = "visualization_cadrl"
metadata = dict(title=name, artist='',
comment=name)
writer_mp4 = animation.FFMpegFileWriter(fps=(3), metadata=metadata)
writer_gif = animation.ImageMagickFileWriter(fps=(3), metadata=metadata)
prediction_step = 8 # 0-7 data 8 =prediction use
def init():
return []
def animate(i):
if i!=prediction_step:
patches = []
ax.patches = []
ax.annotations = []
records = data[:,:(i+1)]
#print(records)
for agent in range(len(records)):
#since we want to show current as well as past points
total_history_len = len(records[agent][:,0])
print("Agent "+str(agent))
for i in range(total_history_len):
patches.append(ax.add_patch( plt.Circle((records[agent][:,0][i]+0.2,records[agent][:,1][i]+0.2),0.2,color=color(agent, ((i/total_history_len)+0.1 )) ,linewidth=0.00001 )))
patches.append(ax.legend(["Timestep "+str(i*10).rjust(6)],loc="upper right", prop={'size': 14},handlelength=0.00001, handletextpad=0.00001, markerfirst=False))
print(i)
return patches
else:
patches = []
records = result
#print(records)
#since we want to show current as well as past points
total_history_len = len(records)
print("Agent "+str(agent_index))
if len(result.shape)==1:
patches.append(ax.add_patch( plt.Circle((records[0]+0.2,records[1]+0.2),0.2,color=[0,0,0,1 ] ,linewidth=0.00001 )))
patches.append(ax.legend(["Prediction".rjust(6)],loc="upper right", prop={'size': 14},handlelength=0.00001, handletextpad=0.00001, markerfirst=False))
else:
for i in range(total_history_len):
patches.append(ax.add_patch( plt.Circle((records[i][0]+0.2,records[i][1]+0.2),0.2,color=[0,0,0,((i/total_history_len)+0.05 ) ] ,linewidth=0.00001 )))
patches.append(ax.legend(["Prediction".rjust(6)],loc="upper right", prop={'size': 14},handlelength=0.00001, handletextpad=0.00001, markerfirst=False))
print(i)
return patches
anim = animation.FuncAnimation(fig, animate,
init_func=init,
frames=9, #total 9 frames => 8 timestep + 1 timestep for showing prediction
interval=333, #ms for each frame
blit=True,
repeat=False)
anim.save(str(name)+'.mp4', writer=writer_mp4)
anim.save(str(name)+'.gif', writer=writer_gif)