我使用了Chainerrl，并尝试了Breakout V0。

我运行此代码。它确实有效，但我的经纪人无法获得奖励（奖励始终低于5分）。

Python 2.7 Ubuntu 14.04

请教我为什么我不能。

我也不明白为什么异端号为972＆gt; l5 = l.linear（972，512）

import chainer 
import chainer.functions as F
import chainer.links as L
import chainerrl
import gym
import numpy as np

from chainer import cuda

import datetime
from skimage.color import rgb2gray
from skimage.transform import resize

env = gym.make('Breakout-v0')
obs = env.reset()

print("observation space   : {}".format(env.observation_space))
print("action space        : {}".format(env.action_space))

action = env.action_space.sample()
obs, r, done, info = env.step(action)
class QFunction(chainer.Chain):
def __init__(self,obs_size, n_action):
    super(QFunction, self).__init__(
        l1=L.Convolution2D(obs_size, 4, ksize=2,pad=1),#210x160
        bn1=L.BatchNormalization(4),
        l2=L.Convolution2D(4, 4, ksize=2,pad=1),#105x80
        bn2=L.BatchNormalization(4),
        #l3=L.Convolution2D(64, 64, ksize=2, pad=1),#100x100
        #bn3=L.BatchNormalization(64),
        #l4=L.Convolution2D(64, 3, ksize=2,pad=1),#50x50
       # bn4=L.BatchNormalization(3),

        l5=L.Linear(972, 512),
        out=L.Linear(512, n_action, initialW=np.zeros((n_action, 512), dtype=np.float32))
    )

def __call__(self, x, test=False):

    h1=F.relu(self.bn1(self.l1(x)))
    h2=F.max_pooling_2d(F.relu(self.bn2(self.l2(h1))),2)
    #h3=F.relu(self.bn3(self.l3(h2)))
    #h4=F.max_pooling_2d(F.relu(self.bn4(self.l4(h3))),2)
    #print h4.shape

    return chainerrl.action_value.DiscreteActionValue(self.out(self.l5(h2)))

n_action = env.action_space.n
obs_size = env.observation_space.shape[0] #(210,160,3)
q_func = QFunction(obs_size, n_action)

optimizer = chainer.optimizers.Adam(eps=1e-2)
optimizer.setup(q_func)

gamma = 0.99

explorer = chainerrl.explorers.ConstantEpsilonGreedy(
epsilon=0.2, random_action_func=env.action_space.sample)

replay_buffer = chainerrl.replay_buffer.ReplayBuffer(capacity=10 ** 6)

phi = lambda x: x.astype(np.float32, copy=False)
agent = chainerrl.agents.DoubleDQN(
q_func, optimizer, replay_buffer, gamma, explorer,
minibatch_size=4, replay_start_size=100, update_interval=10,
target_update_interval=10, phi=phi)

last_time = datetime.datetime.now()
n_episodes = 10000
for i in range(1, n_episodes + 1):
obs = env.reset()

reward = 0
done = False
R = 0

while not done:
    env.render()
    action = agent.act_and_train(obs, reward)
    obs, reward, done, _ = env.step(action)


    if reward != 0:
        R += reward

elapsed_time = datetime.datetime.now() - last_time
print('episode:', i, 
      'reward:', R,
     )
last_time = datetime.datetime.now()

if i % 100 == 0:
    filename = 'agent_Breakout' + str(i)
    agent.save(filename)

agent.stop_episode_and_train(obs, reward, done)
print('Finished.')