Value error with tf_agents. Please help!!!

[Shinwazu]

​import random
from abc import ABC
from random import choice

import numpy as np
from tf_agents.environments import py_environment
from tf_agents.specs import is_continuous
from tf_agents.trajectories import time_step as ts, time_step

class WarehouseEnv(py_environment.PyEnvironment, ABC):

def __init__(self, col, row):
    super(WarehouseEnv).__init__()
    self.ARTICLE_DICT = dict(Cola=1, Sprite=2, Fanta=3, Cola_Zero=4, Cola_Light=5, Mezzo_Mix=6)
    # (ARTICLE_DICT)
    self.ARTICLE = list(self.ARTICLE_DICT.values())
    # print(ARTICLE)

    self.DEMAND_WAREHOUSE = [['1', '2', '3', '1', '1', '1'],
                             ['1', '2'],
                             ['3', '2', '1', '1'],
                             ['4'],
                             ['1', '6', '1']]

    self._episode_ended = False
    self.row = row
    self.col = col
    self._action_spec = is_continuous(spec=np.float64).BoundedArraySpec(shape=(1,), dtype=np.float64, minimum=0,
                                                                        maximum=1,
                                                                        name='action')
    self._observation_spec = is_continuous(spec=np.float64).BoundedArraySpec(shape=(25,), dtype=np.float64,
                                                                             minimum=0,
                                                                             maximum=25, name='observation')
    self.Supply_Warehouse = np.zeros([self.row, self.col], dtype=np.float64)
    print(self.Supply_Warehouse)
    self._state = self.Supply_Warehouse

def _reset(self):
    self._state = self.Supply_Warehouse
    self._episode_ended = False
    return time_step.restart(np.array([self._state], dtype=np.float64))

def action_spec(self):
    return self._action_spec

def observation_spec(self):
    return self._observation_spec

def add_to_supply_warehouse(self):
    for row in range(self.row):
        for col in range(self.col):
            if self.Supply_Warehouse[row][col] == 0:
                if self.Supply_Warehouse[row - 1][col - 1] != 0:
                    self.Supply_Warehouse[row][col] += random.choice(list(self.ARTICLE_DICT.values()))
                    break
            return self._state

def check_demand(self):
    for row in self._state:
        for col in self._state:
            if self._state[row][col] > self._state[:, 1] or self._state[row][col] < self._state[
                row + 1, col + 1] == 0:
                if self._state[row][col] == self.DEMAND_WAREHOUSE[:, :]:
                    return self._state and self.DEMAND_WAREHOUSE

def remove_article(self):
    for row in range(self.row):
        for col in range(self.col):
            if self.check_demand is True:
                self.DEMAND_WAREHOUSE[:, :] = 0
                return self.Supply_Warehouse

def _step(self, action):
    if self._episode_ended:
        return self.reset()
    # action = action.item()
    if action == 0:  # add article to the supply warehouse
        self.add_to_supply_warehouse()
        new_article = random.choice(list(self.ARTICLE_DICT.values()))
        self._state += new_article
        return ts.transition(np.array([self._state], dtype=np.float64), reward=1, discount=0.9)
    if all in self._state > 0:
        self._episode_ended = True
        return ts.termination(np.array([self._state], dtype=np.float64), reward=-9)

    if action == 1:  # Checks demand arbitrarily and remove article from supply warehouse and similar value in
        # Demand list becomes 0
        check = [self.add_to_supply_warehouse, self.check_demand]
        choice(check)()
        if self.check_demand is True:
            self.remove_article()
            return ts.transition(np.array([self._state], dtype=np.float64), reward=5, discount=0.9)
        elif len(self.DEMAND_WAREHOUSE) == 0:
            self._episode_ended = True
            return ts.termination(np.array([self._state], dtype=np.float64), reward=2)
        else:
            return ts.transition(np.array([self._state], dtype=np.float64), reward=0, discount=0.9)

    info = {}
    return self._observation_spec, self._reward, info

2. from tf_agents.environments import tf_py_environment, utils
   from WarehouseEnv import WarehouseEnv

python_environment = WarehouseEnv(5, 7)
utils.validate_py_environment(python_environment, episodes=5)
tf_env = tf_py_environment.TFPyEnvironment(python_environment)

Description:
I'm trying to design and simulate a warehouse environment where adding the article to the warehouse (column wise if the previous column is filled).
Checking demand list and if the article in the demand list (article from '0' index in demand list) matches the articles in warehouse (articles in columns towards the right side) then both values = 0 (i.e., article from warehouse is removed to satisfy demand and hence demand is satisfied, the similar value in demand list is also cleared).
This function after remmoving the article is called removing article.
This cycle contnuous until demand list is 0 or warehouse is full.
rewards are based on how I want the Env to behave.

But I have a value error if I use bounded (Discrete )array:

ValueError: Given "time_step: TimeStep(

{° discount': array(1., dtype=fLoat32),

"observation': array(IL[0, 0, 0, 0, 0],

[0, 0, 0, 0, 0],

[0, 0, 0, 0, 0],

[o,

0, 0, 0, 0],

[0, 0, 0, 0, 0]]], dtype=int32),

*reward': array(0., dtype=FLoat32),

'step_type": array(0, dtype=int32)}) does not match expected 'time_step_spec': Timestep(

{'discount': BoundedArraySpec (shape=), dtype=dtype('FLoat32'), name='discount', minimum=0.0, maximum=1.0),

'observation': BoundedArraySpec (shape=(5, 5), type=dtype ('int32'), name='observation', minimum=0, maximum=10),

"reward': ArraySpec (shape=), dtype=dtype ('FLoat32'), name='reward'),

"step_type': ArraySpec(shape=), dtype=dtype('int32'), name='step_type')})

I tried it by changing the action to continuous which gives me this error:

line 29, in init
self._action_spec = is_continuous(spec=np.float64).BoundedArraySpec(shape=(1,), dtype=np.float64, minimum=0,
AttributeError: 'bool' object has no attribute 'BoundedArraySpec'

I'm in desperate need of help. Please help!!!!