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block2d_pure_icnn_comparison_results.py
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import pickle
from mpl_toolkits import mplot3d
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
from matplotlib import rc
import torch
from envs.block2D import T
import copy
font_size_1 = 12
font_size_2 = 14
font_size_3 = 10
plt.rcParams.update({'font.size': font_size_1})
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
SUCCESS_DIST = 0.025
epoch_start = 0
epoch_num = 50
tm = range(T)
sample_num = 15
base_filename = '/home/shahbaz/Software/garage36/energybased_stable_rl/data/local/experiment'
plt.rcParams["figure.figsize"] = (6,2)
####### rl progress ########33
exps = ['elitebook/cem_energybased_block2d','elitebook/cem_energybased_block2d_1', 'cem_energybased_block2d_9']
color_list = ['b', 'g', 'm']
legend_list = ['$ICNN$', '$QUAD$', '$ICNN+QUAD$']
fig1 = plt.figure()
plt.axis('off')
ax1 = fig1.add_subplot(1, 2, 1)
# ax1.set_title(r'\textbf{(c)}', position=(-0.3, 0.94), fontsize=font_size_2)
ax1.set_xlabel(r'Iteration')
ax1.set_ylabel(r'Reward')
ax1.set_xlim(0,50)
ax1.set_xticks(range(0, 50, 10))
# ax1.set_ylim(-5.0e3,-1.5e3)
ax2 = fig1.add_subplot(1, 2, 2)
ax2.set_ylabel(r'Success \%')
ax2.set_xlabel('Iteration')
# ax2.set_title(r'\textbf{(d)}', position=(-0.25, 0.94), fontsize=font_size_2)
ax2.set_xlim(0,50)
ax2.set_xticks(range(0, 50, 10))
ax2.set_yticks([0,50,100])
ax2.set_ylim(0,100+5)
rewards_undisc_mean = np.zeros(epoch_num)
rewards_undisc_std = np.zeros(epoch_num)
success_mat = np.zeros((epoch_num, sample_num))
block2d_icnn = []
for i in range(len(exps)):
for ep in range(epoch_num):
filename = base_filename + '/' + exps[i] + '/' + 'itr_' + str(ep) + '.pkl'
infile = open(filename, 'rb')
ep_data = pickle.load(infile)
infile.close()
epoch = ep_data['stats'].last_episode
rewards_undisc_mean[ep] = np.mean([np.sum(epoch[s]['rewards']) for s in range(sample_num)])
rewards_undisc_std[ep] = np.std([np.sum(epoch[s]['rewards']) for s in range(sample_num)])
for s in range(sample_num):
# sample = np.min(np.absolute(epoch[s]['observations'][:,:3]), axis=0)
sample = np.min(np.linalg.norm(epoch[s]['observations'][:, :2], axis=1), axis=0)
# sample = epoch[s]['observations'][:, :6]
# success_mat[ep, s] = np.linalg.norm(sample) < SUCCESS_DIST
success_mat[ep, s] = sample < SUCCESS_DIST
success_stat = np.sum(success_mat, axis=1)*(100/sample_num)
rl_progress = {'reward_mean':copy.deepcopy(rewards_undisc_mean),
'reward_std': copy.deepcopy(rewards_undisc_std),
'stats': copy.deepcopy(success_stat)}
block2d_icnn.append(rl_progress)
pickle.dump(block2d_icnn, open("block2d_icnn.p", "wb"))
block2d_icnn = pickle.load( open( "block2d_icnn.p", "rb" ) )
width = 1
offset = [3, 0, 2]
# for i in [0, 1, 2]:
for i in [0, 1, 2]:
rl_progress = block2d_icnn[i]
rewards_undisc_mean = rl_progress['reward_mean']
rewards_undisc_std = rl_progress['reward_std']
success_stat = rl_progress['stats']
interval = 5
# idx = i*width + offset[i]
inds = np.array(range(0,epoch_num)[offset[i]::interval])
# inds = [0] + list(inds)
# del inds[-1]
heights = rewards_undisc_mean[inds]
yerr = rewards_undisc_std[inds]
yerr[0] = 0
idx = np.array(range(0, epoch_num)[i*width::interval])
ax1.errorbar(idx, heights, yerr=yerr, label=legend_list[i], color=color_list[i])
ax1.ticklabel_format(axis="y", style="sci", scilimits=(0,00))
# ax1.legend(prop={'size': font_size_3},frameon=False)
ax1.legend(loc='upper left', bbox_to_anchor=(.5, 1.4), frameon=False, ncol=4, prop={'size': font_size_3})
# inds = np.array(range(0,epoch_num)[idx::interval])
# inds = [0] + list(inds)
# del inds[-1]
success = success_stat[inds]
ax2.bar(idx, success,width, color=color_list[i])
# ax2.legend(prop={'size': font_size_3},frameon=False)
plt.text(0.03, 0.05, '(a)', fontweight='bold', fontsize=font_size_2, transform=plt.gcf().transFigure)
plt.text(0.54, 0.05, '(b)', fontweight='bold', fontsize=font_size_2, transform=plt.gcf().transFigure)
plt.subplots_adjust(left=0.1, bottom=0.23, right=.99, top=0.8, wspace=0.4, hspace=0.7)
fig1.savefig("blocks2d_icnn_vs_quad_rl_progress.pdf")
plt.rcParams["figure.figsize"] = (6,2)
X1_n = 50
X2_n = 50
X1 = np.linspace(-0.5, 0.5, X1_n)
X2 = np.linspace(-0.5, 0.5, X2_n)
X1, X2 = np.meshgrid(X1, X2)
V = np.zeros((X1_n,X2_n))
# pure icnn itr0
exp_name = 'elitebook/cem_energybased_block2d'
filename = base_filename + '/' + exp_name + '/' + 'itr_' + str(0) + '.pkl'
infile = open(filename, 'rb')
ep_data = pickle.load(infile)
infile.close()
epoch = ep_data['stats'].last_episode
Psi = ep_data['algo'].policy._module._icnn_module
for i in range(X1_n):
for j in range(X2_n):
X = np.array([X1[i, j], X2[i, j]])
X = X.astype('float32')
X = torch.from_numpy(X).view(1,2)
V[i,j] = Psi(X)
levels = np.array(range(20))*0.15
fig = plt.figure()
# plt.axis('off')
# plt.legend(loc='upper right', bbox_to_anchor=(2., 1.),frameon=False,ncol=3)
ax1 = fig.add_subplot(1, 3, 1)
ax1.set_xlim(-0.5, 0.5)
ax1.set_xticks([-0.5,0,0.5])
ax1.set_ylim(-0.5, 0.5)
ax1.set_yticks([-0.5,0,0.5])
ax1.contour(X1, X2, V, levels, label='Potential function', colors='b', alpha=0.5)
# ax.plot_wireframe(S1, S2, F, alpha=.7,label='Energy function')
# ax.plot_surface(S1, S2, F,
# cmap='viridis', edgecolor='none',alpha=0.7)
pos = epoch[14]['observations'][:, :2]
ax1.plot(pos[:,0], pos[:,1], color='b',linewidth=2)
ax1.scatter(0,0,color='r',marker='o',s=10)
ax1.set_xlabel(r'$x_1$',fontsize=font_size_2,labelpad=1)
ax1.set_ylabel(r'$x_2$',fontsize=font_size_2, labelpad=0.2)
# ax1.set_title(r'\textbf{(c)}', position=(0.1,0.9), fontsize=font_size_2)
# ax1.legend(loc='upper left', bbox_to_anchor=(-0.15, 1.3),frameon=False,ncol=2,prop={'size': 10})
# pure icnn itr 49
exp_name = 'elitebook/cem_energybased_block2d'
filename = base_filename + '/' + exp_name + '/' + 'itr_' + str(49) + '.pkl'
infile = open(filename, 'rb')
ep_data = pickle.load(infile)
infile.close()
epoch = ep_data['stats'].last_episode
Psi = ep_data['algo'].policy._module._icnn_module
for i in range(X1_n):
for j in range(X2_n):
X = np.array([X1[i, j], X2[i, j]])
X = X.astype('float32')
X = torch.from_numpy(X).view(1,2)
V[i,j] = Psi(X)
levels = np.array(range(20))*0.15
# plt.axis('off')
# plt.legend(loc='upper right', bbox_to_anchor=(2., 1.),frameon=False,ncol=3)
ax1.contour(X1, X2, V, levels, label='Potential function', colors='g', alpha=0.5)
# ax.plot_wireframe(S1, S2, F, alpha=.7,label='Energy function')
# ax.plot_surface(S1, S2, F,
# cmap='viridis', edgecolor='none',alpha=0.7)
pos = epoch[14]['observations'][:, :2]
ax1.plot(pos[:,0], pos[:,1], color='g',linewidth=2)
ax1.scatter(0,0,color='r',marker='o',s=20)
ax1.set_xlabel(r'$x_1$',fontsize=font_size_2,labelpad=1)
ax1.set_ylabel(r'$x_2$',fontsize=font_size_2, labelpad=0.2)
# ax1.set_title(r'\textbf{(c)}', position=(0.1,0.9), fontsize=font_size_2)
# ax1.legend(loc='upper left', bbox_to_anchor=(-0.15, 1.3),frameon=False,ncol=2,prop={'size': 10})
# pure quad itr0
exp_name = 'elitebook/cem_energybased_block2d_1'
filename = base_filename + '/' + exp_name + '/' + 'itr_' + str(0) + '.pkl'
infile = open(filename, 'rb')
ep_data = pickle.load(infile)
infile.close()
epoch = ep_data['stats'].last_episode
S = ep_data['algo'].policy._module._quad_pot_param
for i in range(X1_n):
for j in range(X2_n):
X = np.array([X1[i, j], X2[i, j]])
X = X.astype('float32')
X = torch.from_numpy(X).view(1,2)
V[i,j] = X@torch.diag(S)@X.t()
levels = np.array(range(20))*0.008
# plt.axis('off')
# plt.legend(loc='upper right', bbox_to_anchor=(2., 1.),frameon=False,ncol=3)
ax1 = fig.add_subplot(1, 3, 2)
ax1.set_xlim(-0.5, 0.5)
ax1.set_xticks([-0.5,0,0.5])
ax1.set_ylim(-0.5, 0.5)
ax1.set_yticks([-0.5,0,0.5])
ax1.contour(X1, X2, V, levels, label='Potential function', colors='b', alpha=0.5)
# ax.plot_wireframe(S1, S2, F, alpha=.7,label='Energy function')
# ax.plot_surface(S1, S2, F,
# cmap='viridis', edgecolor='none',alpha=0.7)
pos = epoch[14]['observations'][:, :2]
ax1.plot(pos[:,0], pos[:,1], color='b',linewidth=2)
ax1.scatter(0,0,color='r',marker='o',s=20)
ax1.set_xlabel(r'$x_1$',fontsize=font_size_2,labelpad=1)
ax1.set_ylabel(r'$x_2$',fontsize=font_size_2, labelpad=0.2)
# ax1.set_title(r'\textbf{(c)}', position=(0.1,0.9), fontsize=font_size_2)
# ax1.legend(loc='upper left', bbox_to_anchor=(-0.15, 1.3),frameon=False,ncol=2,prop={'size': 10})
#
# pure quad itr 49
exp_name = 'elitebook/cem_energybased_block2d_1'
filename = base_filename + '/' + exp_name + '/' + 'itr_' + str(49) + '.pkl'
infile = open(filename, 'rb')
ep_data = pickle.load(infile)
infile.close()
epoch = ep_data['stats'].last_episode
S = ep_data['algo'].policy._module._quad_pot_param
for i in range(X1_n):
for j in range(X2_n):
X = np.array([X1[i, j], X2[i, j]])
X = X.astype('float32')
X = torch.from_numpy(X).view(1,2)
V[i,j] = X@torch.diag(S)@X.t()
levels = np.array(range(20))*0.01
# plt.axis('off')
# plt.legend(loc='upper right', bbox_to_anchor=(2., 1.),frameon=False,ncol=3)
ax1.contour(X1, X2, V, levels, label='Potential function', colors='g', alpha=0.5)
# ax.plot_wireframe(S1, S2, F, alpha=.7,label='Energy function')
# ax.plot_surface(S1, S2, F,
# cmap='viridis', edgecolor='none',alpha=0.7)
pos = epoch[14]['observations'][:, :2]
ax1.plot(pos[:,0], pos[:,1], color='g',linewidth=2)
ax1.scatter(0,0,color='r',marker='o',s=20)
ax1.set_xlabel(r'$x_1$',fontsize=font_size_2,labelpad=1)
ax1.set_ylabel(r'$x_2$',fontsize=font_size_2, labelpad=0.2)
# ax1.set_title(r'\textbf{(c)}', position=(0.1,0.9), fontsize=font_size_2)
# icnn + quad itr0
exp_name = 'cem_energybased_block2d_9'
filename = base_filename + '/' + exp_name + '/' + 'itr_' + str(0) + '.pkl'
infile = open(filename, 'rb')
ep_data = pickle.load(infile)
infile.close()
epoch = ep_data['stats'].last_episode
S = ep_data['algo'].policy._module._quad_pot_param
Psi = ep_data['algo'].policy._module._icnn_module
for i in range(X1_n):
for j in range(X2_n):
X = np.array([X1[i, j], X2[i, j]])
X = X.astype('float32')
X = torch.from_numpy(X).view(1,2)
V[i,j] = X@torch.diag(S)@X.t() + Psi(X)
levels = np.array(range(20))*0.15
# plt.axis('off')
# plt.legend(loc='upper right', bbox_to_anchor=(2., 1.),frameon=False,ncol=3)
ax1 = fig.add_subplot(1, 3, 3)
ax1.set_xlim(-0.5, 0.5)
ax1.set_xticks([-0.5,0,0.5])
ax1.set_ylim(-0.5, 0.5)
ax1.set_yticks([-0.5,0,0.5])
ax1.contour(X1, X2, V, levels, label='Potential function', colors='b', alpha=0.5)
# ax.plot_wireframe(S1, S2, F, alpha=.7,label='Energy function')
# ax.plot_surface(S1, S2, F,
# cmap='viridis', edgecolor='none',alpha=0.7)
pos = epoch[14]['observations'][:, :2]
ax1.plot(pos[:,0], pos[:,1], color='b',linewidth=2,label='Itr 0')
ax1.scatter(0,0,color='r',marker='o',s=20)
ax1.set_xlabel(r'$x_1$',fontsize=font_size_2,labelpad=1)
ax1.set_ylabel(r'$x_2$',fontsize=font_size_2, labelpad=0.2)
#
# icnn + quad itr 49
exp_name = 'cem_energybased_block2d_9'
filename = base_filename + '/' + exp_name + '/' + 'itr_' + str(49) + '.pkl'
infile = open(filename, 'rb')
ep_data = pickle.load(infile)
infile.close()
epoch = ep_data['stats'].last_episode
S = ep_data['algo'].policy._module._quad_pot_param
Psi = ep_data['algo'].policy._module._icnn_module
for i in range(X1_n):
for j in range(X2_n):
X = np.array([X1[i, j], X2[i, j]])
X = X.astype('float32')
X = torch.from_numpy(X).view(1,2)
V[i,j] = X@torch.diag(S)@X.t() + Psi(X)
levels = np.array(range(20))*0.15
# plt.axis('off')
# plt.legend(loc='upper right', bbox_to_anchor=(2., 1.),frameon=False,ncol=3)
ax1.contour(X1, X2, V, levels, label='Potential function', colors='g', alpha=0.5)
# ax.plot_wireframe(S1, S2, F, alpha=.7,label='Energy function')
# ax.plot_surface(S1, S2, F,
# cmap='viridis', edgecolor='none',alpha=0.7)
pos = epoch[14]['observations'][:, :2]
ax1.plot(pos[:,0], pos[:,1], color='g',linewidth=2,label='Itr 49')
ax1.scatter(0,0,color='r',marker='o',s=20, label='Goal')
ax1.set_xlabel(r'$x_1$',fontsize=font_size_2,labelpad=1)
ax1.set_ylabel(r'$x_2$',fontsize=font_size_2, labelpad=0.2)
plt.text(0.03, 0.05, '(c)', fontweight='bold', fontsize=font_size_2, transform=plt.gcf().transFigure)
plt.text(0.36, 0.05, '(d)', fontweight='bold', fontsize=font_size_2, transform=plt.gcf().transFigure)
plt.text(0.7, 0.05, '(e)', fontweight='bold', fontsize=font_size_2, transform=plt.gcf().transFigure)
ax1.legend(loc='upper left', bbox_to_anchor=(-2, 1.3),frameon=False,ncol=3,prop={'size': 10})
plt.subplots_adjust(left=0.1, bottom=0.22, right=0.98, top=0.85, wspace=0.5, hspace=.1)
fig.savefig("blocks2d_icnn_potential_function.pdf")