-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathseeing_is_believing.py
171 lines (127 loc) · 6.59 KB
/
seeing_is_believing.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
####################
# IMPORTS
####################
from tensorflow.contrib.layers import *
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import datetime
import time
import sys
from osim.env import *
from osim.http.client import Client
import argparse
import math
####################
# SOME FUNCTIONS
####################
def transform_obs(obs_value, separate_tgtvelocity_field=False):
obs_value_transformed = np.asarray(obs_value)
if separate_tgtvelocity_field:
corr_shape_vel_tgtfield = np.reshape(obs_value_transformed[:242], (2, 11, 11))
center_value = corr_shape_vel_tgtfield[:, 5, 5]
center_value_normalized = center_value/(np.sqrt(center_value[0]*center_value[0] + center_value[1]*center_value[1])*2)
obs_value_transformed = np.hstack([obs_value_transformed[242:]/10, center_value_normalized[0], center_value_normalized[1]])
else:
obs_value_transformed[242:] = obs_value_transformed[242:]/5
obs_value_transformed = obs_value_transformed/10
return obs_value_transformed
class L2M2019EnvStanding(L2M2019Env):
def step(self, action, project=True, obs_as_dict=True):
observation, reward, done, info = super(L2M2019EnvStanding, self).step(action, project=project, obs_as_dict=obs_as_dict)
reward = 0.1 #.d_reward['alive'] --> this isn't working :(
# if not super(L2M2019EnvStanding, self).is_done() and (super(L2M2019EnvStanding, self).osim_model.istep >= super(L2M2019EnvStanding, self).spec.timestep_limit):
# reward += 10
return observation, reward, done, info
####################
# INITIAL SETUP
####################
# Command line parameters
parser = argparse.ArgumentParser(description='Train or test neural net motor controller')
parser.add_argument('--store_trajectories', default=False, action='store_true')
parser.add_argument('--dontvisualize', default=True, action='store_false')
parser.add_argument('--how_many_episodes_to_run', default=3, type=int)
parser.add_argument('--log_path', action='store', default=".")
parser.add_argument('--gamma', default=0.995, type=float)
args = parser.parse_args()
# Load walking environment
env = L2M2019EnvStanding(visualize=args.dontvisualize, difficulty=0)
env.reset()
nb_actions = env.action_space.shape[0]
separate_tgtvelocity_field = True
num_options = 2
####################
# NETWORK CREATION
####################
if separate_tgtvelocity_field:
x = tf.placeholder(tf.float32, shape=(None, 99), name='x')
env_obs_shape = 99
else:
x = tf.placeholder(tf.float32, shape=(None, 339), name='x')
env_obs_shape = 339
weights_init = tf.contrib.layers.variance_scaling_initializer()
bias_init = tf.constant_initializer(0.0)
# ACTOR(s)
a_h1 = fully_connected(inputs=x, num_outputs=444, activation_fn=tf.nn.relu, weights_initializer=weights_init, weights_regularizer=None, biases_initializer=bias_init, scope='a_h1')
a_h2 = fully_connected(inputs=a_h1, num_outputs=444, activation_fn=tf.nn.relu, weights_initializer=weights_init, weights_regularizer=None, biases_initializer=bias_init, scope='a_h2')
a_h3 = fully_connected(inputs=a_h2, num_outputs=444, activation_fn=tf.nn.relu, weights_initializer=weights_init, weights_regularizer=None, biases_initializer=bias_init, scope='a_h3')
actor_heads = []
actor_heads_logits = []
action_choices = np.arange(num_options)/(num_options - 1)
for i in range(nb_actions):
if num_options == 2:
actor_heads_logits.append(tf.concat([fully_connected(inputs=a_h3, num_outputs=1, activation_fn=None, weights_initializer=weights_init, weights_regularizer=None, biases_initializer=bias_init, scope='a' + str(i)), tf.zeros((tf.shape(x)[0], 1))], axis=1))
else:
actor_heads_logits.append(fully_connected(inputs=a_h3, num_outputs=num_options, activation_fn=None, weights_initializer=weights_init, weights_regularizer=None, biases_initializer=bias_init, scope='a' + str(i)))
actor_heads.append(tf.nn.softmax(actor_heads_logits[-1]))
# CRITIC
v_h1 = fully_connected(inputs=x, num_outputs=444, activation_fn=tf.nn.relu, weights_initializer=weights_init, weights_regularizer=None, biases_initializer=bias_init, scope='v_h1')
v_h2 = fully_connected(inputs=v_h1, num_outputs=444, activation_fn=tf.nn.relu, weights_initializer=weights_init, weights_regularizer=None, biases_initializer=bias_init, scope='v_h2')
v_h3 = fully_connected(inputs=v_h2, num_outputs=444, activation_fn=tf.nn.relu, weights_initializer=weights_init, weights_regularizer=None, biases_initializer=bias_init, scope='v_h3')
critic = fully_connected(inputs=v_h3, num_outputs=1, activation_fn=None, weights_initializer=weights_init, weights_regularizer=None, biases_initializer=bias_init, scope='vf')
saver = tf.train.Saver()
####################
# NETWORK TESTING
####################
sess = tf.Session()
saver.restore(sess, args.log_path + "/best_model.ckpt")
discounted_rewards_list = []
value_function_list = []
ep_length_list = []
rewards_list = []
if args.store_trajectories:
saved_obs = []
for i in range(args.how_many_episodes_to_run):
action_supplier = np.zeros(nb_actions)
discounted_reward = 0
total_reward = 0
ep_length = 0
net_gamma = 1
obs = env.reset(obs_as_dict=False)
obs = transform_obs(obs, separate_tgtvelocity_field=True)
done = False
value_function_list.append(sess.run(critic, feed_dict={x: np.reshape(obs, (1, env_obs_shape))})[0][0])
while not done:
if args.store_trajectories:
saved_obs.append(obs)
all_actions_probs = sess.run(actor_heads, feed_dict={x: np.reshape(obs, (1, env_obs_shape))})
action_supplier = np.zeros(nb_actions)
for h in range(nb_actions):
action_supplier[h] = np.random.choice(action_choices, p=all_actions_probs[h][0])
obs, reward, done, info = env.step(action_supplier, obs_as_dict=False)
obs = transform_obs(obs, separate_tgtvelocity_field=True)
ep_length += 1
total_reward += reward
discounted_reward += reward*net_gamma
net_gamma *= args.gamma
ep_length_list.append(ep_length)
rewards_list.append(total_reward)
discounted_rewards_list.append(discounted_reward)
print("%3d Current episode reward: %0.2f"%(i+1, total_reward))
print("\nAverage reward from policy: %0.2f"%(np.mean(rewards_list)))
print("Average episode length from policy: %0.2f \n"%(np.mean(ep_length_list)))
print("Average discounted reward from policy: %0.2f"%(np.mean(discounted_rewards_list)))
print("Average beginning value function from policy: %0.2f"%(np.mean(value_function_list)))
print("Percentage discrepancy: %d%%"%(int((np.abs(np.mean(value_function_list)-np.mean(discounted_rewards_list))/np.mean(discounted_rewards_list))*100)))
if args.store_trajectories:
np.save(args.log_path + "/saved_trajectories", np.asarray(saved_obs))