-
Notifications
You must be signed in to change notification settings - Fork 5
/
train.py
171 lines (131 loc) · 4.98 KB
/
train.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
169
170
171
'''
MIT License
Copyright (c) 2020 Junyoeb Baek
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''
# Implementation of DDPG(Deep Deterministic Policy Gradient)
# on OpenAI gym framwork
import roboschool, gym
import numpy as np, time, os
from tqdm import tqdm
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import argparse
from agent.ddpg import ddpgAgent
NUM_EPISODES_ = 20000
def model_train(pretrained_):
# Create Environments
models = { 'cartpole':"CartPole-v1",
'pendulum':"RoboschoolInvertedPendulum-v1",
'cheetah':"RoboschoolHalfCheetah-v1",
'walker':"RoboschoolWalker2d-v1",
'hopper':"RoboschoolHopper-v1"}
env = gym.make(models['hopper'])
try:
# Ensure action bound is symmetric
assert (np.all(env.action_space.high+env.action_space.low) == 0)
is_discrete = False
print('Continuous Action Space')
except AttributeError:
is_discrete = True
print('Discrete Action Space')
# Create Agent model
agent = ddpgAgent(env, batch_size=128, w_per=False, is_discrete=is_discrete)
if not pretrained_ == None:
agent.load_weights(pretrained_)
# Initialize Environments
steps = 500#env._max_episode_steps # steps per episode
num_act_ = env.action_space.n if is_discrete else env.action_space.shape[0]
num_obs_ = env.observation_space.shape[0]
print("============ENVIRONMENT===============")
print("num_of_action_spaces : %d"%num_act_)
print("num_of_observation_spaces: %d"%num_obs_)
print("max_steps_per_episode: %d"%steps)
print("======================================")
logger = dict(episode=[],reward=[],critic_loss=[])
plt.ion()
fig1 = plt.figure(1); fig2 = plt.figure(2)
ax1 = fig1.add_subplot(111)
ax2 = fig2.add_subplot(111)
try:
act_range = (env.action_space.high - env.action_space.low) / 2 if not is_discrete else 1.
rewards = []; critic_losses = []
max_reward = 0
for epi in range(NUM_EPISODES_):
print("=========EPISODE # %d =========="%epi)
obs = env.reset()
epi_reward = 0
for t in tqdm(range(steps)):
plt.pause(0.01)
# environment rendering on Graphics
env.render()
# Make action from the current policy
a = agent.make_action(obs, t)#env.action_space.sample()#
action = np.argmax(a) if is_discrete else a
# do step on gym at t-time
new_obs, reward, done, info = env.step(action)
# store the results to buffer
agent.memorize(obs, a, reward, done, new_obs)
# grace finish and go to t+1 time
obs = new_obs
epi_reward = epi_reward + reward
agent.replay(1)
# check if the episode is finished
if done or (t == steps-1):
print("Episode#%d, steps:%d, rewards:%f"%(epi,t,epi_reward))
# agent.replay(1)
# save weights at the new records performance
if epi_reward >= max_reward:
max_reward = epi_reward
dir_path = "%s/weights"%os.getcwd()
if not os.path.isdir(dir_path):
os.mkdir(dir_path)
path = dir_path+'/'+'gym_ddpg_'
agent.save_weights(path + 'ep%d_%f'%(epi,max_reward))
# save reward logs
ax1.cla(); ax2.cla();
logger['episode'] = range(epi+1)
logger['reward'].append(epi_reward)
logger['critic_loss'].append(agent.critic.critic_loss)
df = pd.DataFrame(logger)
sns.lineplot(ax=ax1,x='episode',y='reward', data=df)
sns.lineplot(ax=ax2,x='episode',y='critic_loss', data=df)
break;
except KeyboardInterrupt as e: print(e)
finally:
# weight saver
dir_path = "%s/weights"%os.getcwd()
if not os.path.isdir(dir_path):
os.mkdir(dir_path)
path = dir_path+'/'+'gym_ddpg_'
agent.save_weights(path +'lastest')
env.close()
# log saver
import pickle
pickle.dump(logger,open(path+'%s.log'%time.time(),'wb'))
argparser = argparse.ArgumentParser(
description='Train DDPG Agent on the openai gym')
argparser.add_argument(
'-w', '--weights',help='path to pretrained weights')
if __name__ == '__main__':
#################################
# Parse Configurations
#################################
args = argparser.parse_args()
weights_path = args.weights
model_train(pretrained_=weights_path)