-
Notifications
You must be signed in to change notification settings - Fork 7
/
trainer_boxing.py
215 lines (174 loc) · 8.71 KB
/
trainer_boxing.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
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
import tensorflow as tf
import numpy as np
import tensorboardX
import buffer_queue
import collections
import py_process
import wrappers
import config
import model
import time
import gym
flags = tf.app.flags
FLAGS = tf.app.flags.FLAGS
flags.DEFINE_integer('num_actors', 4, 'Number of actors.')
flags.DEFINE_integer('task', -1, 'Task id. Use -1 for local training.')
flags.DEFINE_integer('batch_size', 32, 'how many batch learner should be training')
flags.DEFINE_integer('queue_size', 128, 'fifoqueue size')
flags.DEFINE_integer('trajectory', 20, 'trajectory length')
flags.DEFINE_integer('learning_frame', int(1e9), 'trajectory length')
flags.DEFINE_integer('lstm_size', 256, 'lstm_size')
flags.DEFINE_float('start_learning_rate', 0.0006, 'start_learning_rate')
flags.DEFINE_float('end_learning_rate', 0, 'end_learning_rate')
flags.DEFINE_float('discount_factor', 0.99, 'discount factor')
flags.DEFINE_float('entropy_coef', 0.05, 'entropy coefficient')
flags.DEFINE_float('baseline_loss_coef', 0.5, 'baseline coefficient')
flags.DEFINE_float('gradient_clip_norm', 40.0, 'gradient clip norm')
flags.DEFINE_enum('job_name', 'learner', ['learner', 'actor'], 'Job name. Ignored when task is set to -1')
flags.DEFINE_enum('reward_clipping', 'abs_one', ['abs_one', 'soft_asymmetric'], 'Reward clipping.')
def main(_):
local_job_device = '/job:{}/task:{}'.format(FLAGS.job_name, FLAGS.task)
shared_job_device = '/job:learner/task:0'
is_actor_fn = lambda i: FLAGS.job_name == 'actor' and i == FLAGS.task
is_learner = FLAGS.job_name == 'learner'
cluster = tf.train.ClusterSpec({
'actor': ['localhost:{}'.format(8001+i) for i in range(FLAGS.num_actors)],
'learner': ['localhost:8000']})
server = tf.train.Server(cluster, job_name=FLAGS.job_name, task_index=FLAGS.task)
filters = [shared_job_device, local_job_device]
input_shape = [84, 84, 4]
output_size = 18
available_output_size = 18
env_name = 'BoxingDeterministic-v4'
with tf.device(shared_job_device):
with tf.device('/cpu'):
queue = buffer_queue.FIFOQueue(
FLAGS.trajectory, input_shape, output_size,
FLAGS.queue_size, FLAGS.batch_size,
FLAGS.num_actors, FLAGS.lstm_size)
learner = model.IMPALA(
trajectory=FLAGS.trajectory,
input_shape=input_shape,
num_action=output_size,
discount_factor=FLAGS.discount_factor,
start_learning_rate=FLAGS.start_learning_rate,
end_learning_rate=FLAGS.end_learning_rate,
learning_frame=FLAGS.learning_frame,
baseline_loss_coef=FLAGS.baseline_loss_coef,
entropy_coef=FLAGS.entropy_coef,
gradient_clip_norm=FLAGS.gradient_clip_norm,
reward_clipping=FLAGS.reward_clipping,
model_name='learner',
learner_name='learner',
lstm_hidden_size=FLAGS.lstm_size)
with tf.device(local_job_device):
actor = model.IMPALA(
trajectory=FLAGS.trajectory,
input_shape=input_shape,
num_action=output_size,
discount_factor=FLAGS.discount_factor,
start_learning_rate=FLAGS.start_learning_rate,
end_learning_rate=FLAGS.end_learning_rate,
learning_frame=FLAGS.learning_frame,
baseline_loss_coef=FLAGS.baseline_loss_coef,
entropy_coef=FLAGS.entropy_coef,
gradient_clip_norm=FLAGS.gradient_clip_norm,
reward_clipping=FLAGS.reward_clipping,
model_name='actor_{}'.format(FLAGS.task),
learner_name='learner',
lstm_hidden_size=FLAGS.lstm_size)
sess = tf.Session(server.target)
queue.set_session(sess)
learner.set_session(sess)
if not is_learner:
actor.set_session(sess)
if is_learner:
writer = tensorboardX.SummaryWriter('runs/learner')
train_step = 0
while True:
size = queue.get_size()
if size > 3 * FLAGS.batch_size:
train_step += 1
batch = queue.sample_batch()
s = time.time()
pi_loss, baseline_loss, entropy, learning_rate = learner.train(
state=np.stack(batch.state),
reward=np.stack(batch.reward),
action=np.stack(batch.action),
done=np.stack(batch.done),
behavior_policy=np.stack(batch.behavior_policy),
previous_action=np.stack(batch.previous_action),
initial_h=np.stack(batch.previous_h),
initial_c=np.stack(batch.previous_c))
writer.add_scalar('data/pi_loss', pi_loss, train_step)
writer.add_scalar('data/baseline_loss', baseline_loss, train_step)
writer.add_scalar('data/entropy', entropy, train_step)
writer.add_scalar('data/learning_rate', learning_rate, train_step)
writer.add_scalar('data/time', time.time() - s, train_step)
print('training')
else:
trajectory_data = collections.namedtuple(
'trajectory_data',
['state', 'next_state', 'reward', 'done',
'action', 'behavior_policy', 'previous_action',
'initial_h', 'initial_c'])
env = wrappers.make_uint8_env(env_name)
if FLAGS.task == 0:
env = gym.wrappers.Monitor(env, 'save-mov', video_callable=lambda episode_id: episode_id%10==0)
state = env.reset()
previous_action = 0
previous_h = np.zeros([FLAGS.lstm_size])
previous_c = np.zeros([FLAGS.lstm_size])
episode = 0
score = 0
episode_step = 0
total_max_prob = 0
writer = tensorboardX.SummaryWriter('runs/{}/actor_{}'.format(env_name, FLAGS.task))
while True:
unroll_data = trajectory_data(
[], [], [], [],
[], [], [] ,[], [])
actor.parameter_sync()
for _ in range(FLAGS.trajectory):
action, behavior_policy, max_prob, h, c = actor.get_policy_and_action(
state, previous_action, previous_h, previous_c)
episode_step += 1
total_max_prob += max_prob
next_state, reward, done, info = env.step(action % available_output_size)
score += reward
unroll_data.state.append(state)
unroll_data.next_state.append(next_state)
unroll_data.reward.append(reward)
unroll_data.done.append(done)
unroll_data.action.append(action)
unroll_data.behavior_policy.append(behavior_policy)
unroll_data.previous_action.append(previous_action)
unroll_data.initial_h.append(previous_h)
unroll_data.initial_c.append(previous_c)
state = next_state
previous_action = action
previous_h = h
previous_c = c
if done:
print(episode, score)
writer.add_scalar('data/{}/prob'.format(env_name), total_max_prob / episode_step, episode)
writer.add_scalar('data/{}/score'.format(env_name), score, episode)
writer.add_scalar('data/{}/episode_step'.format(env_name), episode_step, episode)
episode += 1
score = 0
episode_step = 0
total_max_prob = 0
state = env.reset()
previous_action = 0
previous_h = np.zeros([FLAGS.lstm_size])
previous_c = np.zeros([FLAGS.lstm_size])
queue.append_to_queue(
task=FLAGS.task, unrolled_state=unroll_data.state,
unrolled_next_state=unroll_data.next_state, unrolled_reward=unroll_data.reward,
unrolled_done=unroll_data.done, unrolled_action=unroll_data.action,
unrolled_behavior_policy=unroll_data.behavior_policy,
unrolled_previous_action=unroll_data.previous_action,
unrolled_previous_h=unroll_data.initial_h,
unrolled_previous_c=unroll_data.initial_c)
if __name__ == '__main__':
tf.app.run()