-
Notifications
You must be signed in to change notification settings - Fork 1
/
a3c-flappy_sota.py
187 lines (150 loc) · 8.31 KB
/
a3c-flappy_sota.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
from multiprocessing import Process, Lock, Value, Array, Queue
import os
import ctypes
import tensorflow as tf
import numpy as np
import pandas as pd
import models.A2C as A2C
import envs.cartPole as cartPole
import envs.flappyBird as flappyBird
import models.util as Util
class A3C:
def __init__(self):
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["SDL_VIDEODRIVER"] = "dummy"
os.environ["SDL_AUDIODRIVER"] = "dummy"
tf.config.set_soft_device_placement(True)
def worker(self, proc_id, worker_id, global_remain_episode, global_alive_workers, global_grad_queue, global_var_queue, global_res_queue):
print(f'Process {proc_id} Worker {worker_id} start')
gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
# tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=10240)])
with tf.device("/CPU:0"):
local_agent, local_env = self.init_agent_env(proc_id, 'worker', worker_id)
# Reset the weight back to checkpoint
ckpt = tf.train.Checkpoint(model=local_agent.model, opt=local_agent.optimizer)
recorder = Util.Recorder(ckpt=ckpt, ckpt_path='results/ckpt', plot_title='A3C FlappyBird', filename='results/a3c_flappy', save_period=5000)
ep = recorder.restore()
# Copy model from the global agent
global_vars = global_var_queue.get()
local_agent.model.set_weights(global_vars)
# Reset Game State
state = local_env.reset()
while global_remain_episode.value > 0:
episode_reward, loss, gradients, trajectory, is_over = local_agent.train_on_env(env = local_env, cal_gradient_vars = None)
# print(f'Episode {global_remain_episode.value} Reward with worker {worker_id}: {episode_reward}')
global_res_queue.put({'loss': loss, 'reward': episode_reward, 'worker_id': worker_id})
global_grad_queue.put({'loss': loss, 'reward': episode_reward, 'gradients': gradients, 'worker_id': worker_id})
if not global_var_queue.empty():
global_vars = global_var_queue.get()
local_agent.model.set_weights(global_vars)
# local_agent.model.set_weights(global_vars['model'])
# local_agent.optimizer.set_weights(global_vars['opt'])
# print(f'Worker {worker_id} Update Weights')
with global_remain_episode.get_lock():
global_remain_episode.value -= 1
with global_alive_workers.get_lock():
global_alive_workers.value -= 1
print(f"Worker {worker_id} done")
def param_server(self, proc_id, ps_id, global_remain_episode, global_alive_workers, global_grad_queue, global_var_queues):
gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
# tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=10240)])
with tf.device("/CPU:0"):
global_agent, env = self.init_agent_env(proc_id, 'ps', ps_id)
# Setup recorder
ckpt = tf.train.Checkpoint(model=global_agent.model, opt=global_agent.optimizer)
recorder = Util.Recorder(ckpt=ckpt, ckpt_path='results/ckpt', plot_title='A3C FlappyBird', filename='results/a3c_flappy', save_period=5000)
ep = recorder.restore()
print(f"Restore {ep}")
with global_remain_episode.get_lock():
global_remain_episode.value = global_remain_episode.value - ep
# Copy model to the local agent
model_weights = global_agent.model.get_weights()
for i in range(len(global_var_queues)):
global_var_queues[i].put(model_weights)
while ((not global_grad_queue.empty()) or (global_alive_workers.value > 0)):
if not global_grad_queue.empty():
# print(f'Getting gradients from queue')
item = global_grad_queue.get()
global_agent.update(loss = item['loss'], gradients = item['gradients'])
recorder.record(float(item['loss']), float(item['reward']))
model_weights = global_agent.model.get_weights()
# opt_weights = global_agent.optimizer.get_weights()
for i in range(len(global_var_queues)):
if not global_var_queues[i].full():
global_var_queues[i].put(model_weights)
# global_var_queues[i].put({'model': model_weights, 'opt': opt_weights})
# print(f'Put vars in queue for worker {i}')
print("Complete PS apply")
for queue in global_var_queues:
if not queue.empty():
queue.get()
# print(f'Clear vars in queue for worker')
print(f'PS {ps_id} done')
def init_agent_env(self, proc_id, role, role_id):
# env = cartPole.CartPoleEnv()
env = flappyBird.FlappyBirdEnv()
NUM_STATE_FEATURES = env.get_num_state_features()
NUM_ACTIONS = env.get_num_actions()
LEARNING_RATE = 0.0001
REWARD_DISCOUNT = 0.99
COEF_VALUE= 1
COEF_ENTROPY = 0
agent = A2C.Agent((NUM_STATE_FEATURES, ), NUM_ACTIONS, REWARD_DISCOUNT, LEARNING_RATE, COEF_VALUE, COEF_ENTROPY)
return agent, env
# def is_having_training_info(self):
# return ((not global_res_queue.empty()) or (global_alive_workers.value > 0))
def get_res(self, global_res_queue, global_alive_workers):
if ((not global_res_queue.empty()) or (global_alive_workers.value > 0)):
return global_res_queue.get()
else:
return None
def start(self):
# print(tf.config.experimental.list_physical_devices(device_type=None))
# print(tf.config.experimental.list_logical_devices(device_type=None))
self.episode_num = 3000000
self.ps_num = 1
self.worker_num = 10
self.current_episode = 1
global_remain_episode = Value('i', self.episode_num)
global_alive_workers = Value('i', self.worker_num)
global_res_queue = Queue()
global_grad_queue = Queue()
global_var_queues = [Queue(1) for i in range(self.worker_num)]
pss = []
workers = []
episode_results = []
for ps_id in range(self.ps_num):
pss.append(Process(target = self.param_server, args=(ps_id, ps_id, global_remain_episode, global_alive_workers, global_grad_queue, global_var_queues)))
for worker_id in range(self.worker_num):
workers.append(Process(target = self.worker, args=(worker_id + self.ps_num, worker_id, global_remain_episode, global_alive_workers, global_grad_queue, global_var_queues[worker_id], global_res_queue)))
for num in range(self.ps_num):
pss[num].start()
for num in range(self.worker_num):
workers[num].start()
while ((not global_res_queue.empty()) or (global_alive_workers.value > 0)):
if not global_res_queue.empty():
episode_results.append(global_res_queue.get())
episode_res = episode_results.pop(0)
print(f"Episode {self.current_episode} Reward with worker {episode_res['worker_id']}: {episode_res['reward']}\t| Loss: {episode_res['loss']}")
self.current_episode += 1
global_grad_queue.close()
global_grad_queue.join_thread()
global_res_queue.close()
global_res_queue.join_thread()
for queue in global_var_queues:
queue.close()
queue.join_thread()
for num in range(self.worker_num):
workers[num].join()
print(f'Worker {num} join')
for num in range(self.ps_num):
pss[num].join()
print(f'PS {num} join')
if __name__ == '__main__':
# print(tf.config.experimental.list_physical_devices(device_type=None))
# print(tf.config.experimental.list_logical_devices(device_type=None))
a3c = A3C()
a3c.start()