py_environment 'time_step' doesn't match 'time_step_spec'

Question

I have created a custom pyenvironment via tf agents. However I can't validate the environment or take steps within it with py_policy.action I'm confused as to what is excepted from the time_step_specs

I have tried converting to tf_py_environment via tf_py_environment.TFPyEnvironment and was successful in taking actions with tf_policy but I'm still confused as to the difference.

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)

---

This is the error I get:

ValueError Traceback (most recent call last) in ----> 1 utils.validate_py_environment(env, episodes=5)

C:\Users\bzhang\AppData\Local\Continuum\anaconda3\lib\site-packages\tf_agents\environments\utils.py in 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.]])) does not match expected 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, maximum=1))

Answer 1

Your observation does not match the spec, you need to pass dtype=np.int32 to the np array to make sure the type match.