我通过 tf 代理创建了一个自定义 pyenvironment。但是我无法使用 py_policy.action 验证环境或在其中采取步骤我对 time_step_specs 的例外情况感到困惑
我尝试通过 tf_py_environment.TFPyEnvironment 转换为 tf_py_environment 并成功地使用 tf_policy 采取行动,但我仍然对差异感到困惑。
import abc
import numpy as np
from tf_agents.environments import py_environment
from tf_agents.environments import tf_environment
from tf_agents.environments import tf_py_environment
from tf_agents.environments import utils
from tf_agents.specs import array_spec
from tf_agents.environments import wrappers
from tf_agents.trajectories import time_step as ts
from tf_agents.policies import random_tf_policy
import tensorflow as tf
import tf_agents
class TicTacToe(py_environment.PyEnvironment):
def __init__(self,n):
super(TicTacToe,self).__init__()
self.n = n
self.winner = None
self._episode_ended = False
self.inital_state = np.zeros((n,n))
self._state = self.inital_state
self._observation_spec = array_spec.BoundedArraySpec(
shape = (n,n),dtype='int32',minimum = -1,maximum = 1,name =
'TicTacToe board state spec')
self._action_spec = array_spec.BoundedArraySpec(
shape = (),dtype = 'int32', minimum = 0,maximum = 8, name =
'TicTacToe action spec')
def observation_spec(self):
return self._observation_spec
def action_spec(self):
return self._action_spec
def _reset(self):
return ts.restart(self.inital_state)
def check_game_over(self):
for i in range(self.n):
if (sum(self._state[i,:])==self.n) or
(sum(self._state[:,i])==self.n):
self.winner = 1
return True
elif (sum(self._state[i,:])==-self.n) or
(sum(self._state[:,i])==-self.n):
self.winner = -1
return True
if (self._state.trace()==self.n) or
(self._state[::-1].trace()==self.n):
self.winner = 1
return True
elif (self._state.trace()==-self.n) or (self._state[::-1].trace()==-
self.n):
self.winner = -1
return True
if not (0 in self._state):
return True
def _step(self,action):
self._state[action//3,action%3]=1
self._episode_ended = self.check_game_over
if self._episode_ended==True:
if self.winner == 1:
reward = 1
elif self.winner == None:
reward = 0
else:
reward = -1
return ts.termination(self._state,dtype = 'int32',reward=reward)
else:
return ts.transition(self._state,dtype = 'int32',reward =
0.0,discount = 0.9)
env = TicTacToe(3)
utils.validate_py_environment(env, episodes=5)
这是我得到的错误:
----> 1 utils.validate_py_environment(env, episodes=5) 中的 ValueError Traceback (最近一次调用最后一次)
C:\Users\bzhang\AppData\Local\Continuum\anaconda3\lib\site-packages\tf_agents\environments\utils.py 在 validate_py_environment(environment, episodes) 58 raise ValueError( 59 'Given time_step
: %r does not match expected time_step_spec
: %r' % ---> 60 (time_step, time_step_spec)) 61 62 action = random_policy.action(time_step).action
ValueError: Given time_step
: TimeStep(step_type=array(0), reward=array(0., dtype=float32), discount=array(1., dtype=float32), observation=array([[0., 0., 0 .], [0., 0., 0.], [0., 0., 0.]])) 与预期不匹配time_step_spec
:TimeStep(step_type=ArraySpec(shape=(), dtype=dtype('int32' ), name='step_type'), reward=ArraySpec(shape=(), dtype=dtype('float32'), name='reward'), discount=BoundedArraySpec(shape=(), dtype=dtype('float32' ), name='discount', minimum=0.0, maximum=1.0),observation=BoundedArraySpec(shape=(3, 3), dtype=dtype('int32'), name='TicTacToe board state spec', minimum=- 1,最大值=1))