问题描述
我正在尝试使用 Keras 在 MineRL 环境中训练代理。 到目前为止，这是我的代码：
import gym
import random
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Flatten,Convolution2D
from tensorflow.keras.optimizers import Adam
from rl.agents import DQNAgent
from rl.memory import SequentialMemory
from rl.policy import LinearannealedPolicy,epsgreedyQPolicy
from gym import spaces
import minerl
from collections import OrderedDict
class EnvHandeler():
def __init__(self):
self.env = gym.make('mineRLTreechop-v0')
self.env.observation_space = gym.spaces.Dict({
"pov": gym.spaces.Box(low=0,high=255,shape=(64,64,3))
})
self.action_space = self.env.action_space
self.observation_space = self.env.observation_space
def step(self,action):
action_holder = self.env.action_space.noop()
if action == 0:
action_holder['forward'] = 1
action_holder['jump'] = 0
action_holder['left'] = 0
action_holder['right'] = 0
action_holder['attack'] = 0
if action == 1:
action_holder['forward'] = 1
action_holder['jump'] = 1
action_holder['left'] = 0
action_holder['right'] = 0
action_holder['attack'] = 0
if action == 2:
action_holder['forward'] = 0
action_holder['jump'] = 0
action_holder['left'] = 1
action_holder['right'] = 0
action_holder['attack'] = 0
if action == 3:
action_holder['forward'] = 0
action_holder['jump'] = 0
action_holder['left'] = 0
action_holder['right'] = 1
action_holder['attack'] = 0
if action == 4:
action_holder['forward'] = 0
action_holder['jump'] = 0
action_holder['left'] = 0
action_holder['right'] = 0
action_holder['attack'] = 1
n_state,reward,done,info = self.env.step(action_holder)
return n_state,info
def render(self):
self.env.render()
def reset(self):
#self.env.reset()
return self.env.reset()
def build_model(height,width,channels,actions):
model = Sequential()
print(height,channels)
model.add(Convolution2D(32,(8,8),strides=(4,4),activation='relu',input_shape=(3,height,channels)))
model.add(Convolution2D(64,(4,strides=(2,2),activation='relu'))
model.add(Convolution2D(64,(3,3),activation='relu'))
model.add(Flatten())
model.add(Dense(512,activation='relu'))
model.add(Dense(256,activation='relu'))
model.add(Dense(actions,activation='linear'))
return model
def build_agent(model,actions):
policy = LinearannealedPolicy(epsgreedyQPolicy(),attr='eps',value_max=1.,value_min=.1,value_test=.2,nb_steps=10000)
memory = SequentialMemory(limit=1000,window_length=3)
dqn = DQNAgent(model=model,memory=memory,policy=policy,enable_dueling_network=True,dueling_type='avg',nb_actions=actions,nb_steps_warmup=1000
)
return dqn
def main():
env = EnvHandeler()
height,channels = env.observation_space['pov'].shape
actions = 5
model = build_model(height,actions)
dqn = build_agent(model,actions)
dqn.compile(Adam(lr=1e-4))
print(model.summary())
dqn.fit(env,nb_steps=10000,visualize=False,verbose=2)
if __name__ == '__main__':
main()
我在开始训练时收到以下错误：
Training for 10000 steps ...
Traceback (most recent call last):
File "F:/WORKING/minecraft KI/Project/SmartTreeKeras.py",line 172,in <module>
main()
File "F:/WORKING/minecraft KI/Project/SmartTreeKeras.py",line 159,in main
dqn.fit(env,verbose=2)
File "C:\Users\Marius\anaconda3\lib\site-packages\rl\core.py",line 169,in fit
action = self.forward(observation)
File "C:\Users\Marius\anaconda3\lib\site-packages\rl\agents\dqn.py",line 225,in forward
q_values = self.compute_q_values(state)
File "C:\Users\Marius\anaconda3\lib\site-packages\rl\agents\dqn.py",line 69,in compute_q_values
q_values = self.compute_batch_q_values([state]).flatten()
File "C:\Users\Marius\anaconda3\lib\site-packages\rl\agents\dqn.py",line 64,in compute_batch_q_values
q_values = self.model.predict_on_batch(batch)
File "C:\Users\Marius\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training_v1.py",line 1201,in predict_on_batch
x,extract_tensors_from_dataset=True)
File "C:\Users\Marius\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training_v1.py",line 2334,in _standardize_user_data
batch_size=batch_size)
File "C:\Users\Marius\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training_v1.py",line 2361,in _standardize_tensors
exception_prefix='input')
File "C:\Users\Marius\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training_utils.py",line 573,in standardize_input_data
'with shape ' + str(data_shape))
ValueError: Error when checking input: expected conv2d_input to have 5 dimensions,but got array with shape (1,3)``
正如评论中所问的 env.step() 返回：
The env.stop() returns: (OrderedDict([('pov',array([[[120,153,216],[120,217],...,[121,215],214],214]],[[121,213],213]],[[ 16,30,9],[ 14,27,8],[ 13,25,7],[ 17,33,10],[ 21,40,12],10]],[[ 14,41,[ 28,54,16],[ 16,8]],[[ 12,24,[ 20,39,11],42,[ 33,19],[ 30,58,17],34,10]]],dtype=uint8))]),0.0,False,{})
更多数据：
- Python 3.7
- tensorflow-gpu 2.3.0
我可以想象结构中也存在错误，因为预期尺寸会根据输入不断变化，例如：如果我输入 5 个 Dims，它需要 6 个 Dims，如果我输入 3 个 Dims，它需要 4 个等等。
有点为这个错误发疯。 我已经尝试了许多在网上找到的解决方案，但都没有奏效。
如果有人能帮忙我会很高兴！
-干杯
解决方法
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