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a3c-cartpole.py
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a3c-cartpole.py
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from multiprocessing import Process, Lock, Value, Array, Queue
import os
import ctypes
import tensorflow as tf
import numpy as np
# import tensorflow.compat.v1 as tfv1
import models.A2C as A2C
import envs.cartPole as cartPole
import envs.flappyBird as flappyBird
class Master:
def __init__(self):
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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):
# server = tf.distribute.Server(cluster_spec, 'worker', worker_id)
print(f'Process {proc_id} Worker {worker_id} start')
# config = tf.compat.v1.ConfigProto()
# config.gpu_options.allow_growth=True
# with tfv1.Session(target = server.target, config=config).as_default() as sess:
# with tf.device("/job:localhost/replica:0/task:0/device:CPU:0"):
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)
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, '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)
# 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):
# server = tf.distribute.Server(cluster_spec, 'ps', ps_id)
# config = tf.compat.v1.ConfigProto()
# config.gpu_options.allow_growth=True
# with tfv1.Session(target = server.target, config=config).as_default() as sess:
# with tf.device("/job:localhost/replica:0/task:0/device:CPU:0"):
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)
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'])
weights = global_agent.model.get_weights()
for i in range(len(global_var_queues)):
if not global_var_queues[i].full():
global_var_queues[i].put(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()
PRINT_EVERY_EPISODE = 20
LEARNING_RATE = 0.003
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 make_cluster_config(self, ps_num, worker_num, host = 'localhost', base_port = 8000):
cluster_config = {
'worker': [],
'ps': []
}
port = base_port
for ps_id in range(self.ps_num):
cluster_config['ps'].append(f'{host}:{port}')
port += 1
for worker_id in range(self.worker_num):
cluster_config['worker'].append(f'{host}:{port}')
port += 1
return cluster_config
# 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 = 200
self.ps_num = 1
self.worker_num = 2
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)]
cluster_config = self.make_cluster_config(self.ps_num, self.worker_num)
pss = []
workers = []
episode_results = []
cluster_spec = tf.train.ClusterSpec(cluster_config)
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))
m = Master()
m.start()