def main():
persistence = Persistence()
env = BitEnvironment()
direct = [[-1, -1], [-1, 0], [-1, 1], [0, -1], [0, 1], [1, -1], [1, 0],
[1, 1]]
terminal, step_count, e, success, = False, 0, 0, 0
obes = []
for i in range(0, 80):
obes.append([random.randint(0, 99), random.randint(0, 99)])
env.setObstacle(obes)
while True:
#direction = random.randint(0, 7)
# 包含两个，01方向减法，23上次速度
direction = env.getNextStep()
Position, move = env.step(direction)
env.preDirect = direct[direction]
print("行动否：{} 具体行动方向: {} 当前位置 {}".format(move, direct[direction],
Position))
dist, realDirect = env.distToTarget()
print("距离目标: {} 目标方位: {}".format(dist, realDirect))
if env.judgeCollision() or env.done():
terminal = True
step_count += 1
print("阶段 {} 完成, 总成功: {} 当轮步数: {}".format(e, success, step_count))
env.showimage()
if terminal:
if env.done():
success += 1
print(
"-----------------------------success----------------------------------"
)
elif env.judgeCollision():
print(
"-----------------------------发生碰撞----------------------------------"
)
step_count = 0
e += 1
infoShow = "time {}, episode {} finish, total success: {} step: {}".format(
time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time())), e, success,
step_count)
persistence.saveTerminalRecord("GoInBitMap", infoShow)
env.reset([random.randint(0, 99), random.randint(0, 99)])
env.setObstacle(obes)
terminal = False
time.sleep(0.2)
def main():
env = drone_env_heightcontrol(aim=None)
state = env.reset()
state_shape = 4
action_bound = 1
action_dim = 2
e, success, episode_reward, step_count = 0, 0, 0, 0
persistence = Persistence()
if choose == "1":
infoShow = "time {}, start new train, method{}".format(
time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())),
"DDPG")
persistence.saveTerminalRecord("DDPG", infoShow)
with tf.device("/gpu:0"):
config = tf.ConfigProto(
allow_soft_placement=True
) # tf.ConfigProto()函数用在创建session的时候，用来对session进行参数配置
config.gpu_options.allow_growth = True # 当allow_growth设置为True时，分配器将不会指定所有的GPU内存，而是根据需求增长
with tf.Session(config=config) as sess:
globe_episode = tf.Variable(
0, dtype=tf.int32, trainable=False, name='globe_episode'
) # 设定trainable=False 可以防止该变量被数据流图的 GraphKeys.TRAINABLE_VARIABLES 收集, # 这样我们就不会在训练的时候尝试更新它的值。
agent = DDPG_agent(sess, state_shape, action_bound, action_dim)
saver = tf.train.Saver(var_list=tf.global_variables())
print(DIR)
if not agent.load(saver, DIR):
sess.run(tf.global_variables_initializer())
if not os.path.exists(DIR):
os.mkdir(DIR)
else:
print("coninnue------------------")
if PREMODEL:
prepath = os.path.join(PATH, 'premodel\checkpoint')
ckpt = tf.train.get_checkpoint_state(
os.path.dirname(prepath))
if ckpt and ckpt.model_checkpoint_path:
saver.restore(agent.sess, ckpt.model_checkpoint_path)
agent.action_noise.reset()
print(
"------------pretrained model loaded-------------")
while True:
print(state[1])
action = agent.act(state)
next_state, reward, terminal, info = env.step(action)
episode_reward += reward
agent.observe(state, action, reward, next_state, terminal)
agent.train()
state = next_state
'''
if train%10==0:
print("total training episode: {}".format(train))
print("--------------------------------------------------------------------")
if train%10000==0:
nDir = os.path.join(PATH, "data/" + str(int(train // 10000)))
if not os.path.exists(nDir):
os.mkdir(nDir)
agent.save(saver, nDir)
print("save")
print("--------------------------------------------------------------------")
train+=1
'''
step_count += 1
print("aim height: {}".format(env.aim_height).ljust(
20, " "),
"reward: {:.5f}.".format(reward).ljust(20, " "),
"steps: {}".format(step_count).ljust(20, " "),
end="\r")
if terminal:
if info == "success":
success += 1
print(" " * 80, end="\r")
print(
"episode {} finish, average reward: {:.5f}, total success: {} result: {} step: {}"
.format(e, step_count).ljust(80, " "))
episode_reward = 0
step_count = 0
e += 1
total_episode = sess.run(globe_episode.assign_add(1))
if e % 10 == 0:
nDir = os.path.join(PATH,
"data/" + str(int(e // 10)))
if not os.path.exists(nDir):
os.mkdir(nDir)
agent.save(saver, nDir, DIR)
print("total training episode: {}".format(
total_episode))
infoShow = "time {}, episode {} finish, total success: {} step: {}".format(
time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time())), e,
success, step_count)
persistence.saveTerminalRecord("DDPG", infoShow)
state = env.reset()
return
elif choose == "2":
agent = EnergyAgent(state_shape, action_bound, action_dim)
infoShow = "time {}, start new train, method{}".format(
time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())),
"EnergyAgent")
persistence.saveTerminalRecord("EnergyAgent", infoShow)
while True:
# 包含两个，01方向减法，23上次速度
print("目标相对位置，目前位移方向: {} ".format(state[1]))
action = agent.act(state)
print("具体方向: {} ".format(action))
next_state, reward, terminal, info = env.step(action)
state = next_state
step_count += 1
print("阶段 {} 完成, 总成功: {} 当轮步数: {}".format(e, success, step_count))
if terminal:
if info == "success":
success += 1
print(
"-----------------------------success----------------------------------"
)
step_count = 0
e += 1
infoShow = "time {}, episode {} finish, total success: {} step: {}".format(
time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time())), e, success,
step_count)
persistence.saveTerminalRecord("EnergyAgent", infoShow)
state = env.reset()
else:
print("仿真结束")
return
