深度强化学习实战教程：使用PaddlePaddle（飞桨）实现DDPG算法在PARL框架中的应用

class Agent(parl.Agent):
def __init__(self, algorithm, obs_dim, act_dim):
assert isinstance(obs_dim, int)
assert isinstance(act_dim, int)
self.obs_dim = obs_dim
self.act_dim = act_dim
super(Agent, self).__init__(algorithm)

# 注意：最开始先同步self.model和self.target_model的参数.
self.alg.sync_target(decay=0)

def build_program(self):
self.pred_program = fluid.Program()
self.learn_program = fluid.Program()

with fluid.program_guard(self.pred_program):
obs = layers.data(
name='obs', shape=[self.obs_dim], dtype='float32')
self.pred_act = self.alg.predict(obs)

with fluid.program_guard(self.learn_program):
obs = layers.data(
name='obs', shape=[self.obs_dim], dtype='float32')
act = layers.data(
name='act', shape=[self.act_dim], dtype='float32')
reward = layers.data(name='reward', shape=[], dtype='float32')
next_obs = layers.data(
name='next_obs', shape=[self.obs_dim], dtype='float32')
terminal = layers.data(name='terminal', shape=[], dtype='bool')
_, self.critic_cost = self.alg.learn(obs, act, reward, next_obs,
terminal)

def predict(self, obs):
obs = np.expand_dims(obs, axis=0)
act = self.fluid_executor.run(
self.pred_program, feed={'obs': obs},
fetch_list=[self.pred_act])[0]
act = np.squeeze(act)
return act

def learn(self, obs, act, reward, next_obs, terminal):
feed = {
'obs': obs,
'act': act,
'reward': reward,
'next_obs': next_obs,
'terminal': terminal
}
critic_cost = self.fluid_executor.run(
self.learn_program, feed=feed, fetch_list=[self.critic_cost])[0]
self.alg.sync_target()
return critic_cost