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QuadrotorAgent.py
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QuadrotorAgent.py
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import paddle.fluid as fluid
import parl
from parl import layers
import gym
import numpy as np
class QuadrotorAgent(parl.Agent):
def __init__(self, algorithm, obs_dim, act_dim=5):
assert isinstance(obs_dim, int)
assert isinstance(act_dim, int)
self.obs_dim = obs_dim
self.act_dim = act_dim
super(QuadrotorAgent, self).__init__(algorithm)
# 注意,在最开始的时候,先完全同步target_model和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]
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