AttributeError: type object 'FooEnv' has no attribute 'reset'

Question

I am new in Python and I faced with a problem in my code. I try to build my custom environment for a Deep Q-Network program. The name of my environment is "FooEnv".But when I run the main code, I faced with this error in line FooEnv.reset()

type object 'FooEnv' has no attribute 'reset'

This is my main code, That I call "FooEnv" here:

import json
import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense
from keras.optimizers import sgd
from FooEnv import FooEnv
class ExperienceReplay(object):
  def __init__(self, max_memory=100, discount=.9):
    self.max_memory = max_memory
    self.memory = list()
    self.discount = discount

  def remember(self, states, game_over):
    # memory[i] = [[state_t, action_t, reward_t, state_t+1], game_over?]
    self.memory.append([states, game_over])
    if len(self.memory) > self.max_memory:
        del self.memory[0]

  def get_batch(self, model, batch_size=10):
    len_memory = len(self.memory)
    num_actions = model.output_shape[-1]
    # env_dim = self.memory[0][0][0].shape[1]
    env_dim = self.memory[0][0][0].shape[1]
    inputs = np.zeros((min(len_memory, batch_size), env_dim))
    targets = np.zeros((inputs.shape[0], num_actions))
    for i, idx in enumerate(np.random.randint(0, len_memory,
                                              size=inputs.shape[0])):
        state_t, action_t, reward_t, state_tp1 = self.memory[idx][0]
        game_over = self.memory[idx][1]

        inputs[i:i+1] = state_t
        # There should be no target values for actions not taken.
        # Thou shalt not correct actions not taken #deep
        targets[i] = model.predict(state_t)[0]
        Q_sa = np.max(model.predict(state_tp1)[0])
        if game_over:  # if game_over is True
            targets[i, action_t] = reward_t
        else:
            # reward_t + gamma * max_a' Q(s', a')
            targets[i, action_t] = reward_t + self.discount * Q_sa
    return inputs, targets
if __name__ == "__main__":
    # parameters
    epsilon = .1  
    num_actions = 2  
    epoch = 1000
    max_memory = 500
    hidden_size = 100
    batch_size = 50
    input_size = 2
    f_c=[2.4*10**9]
    eta_Los=[1]
    eta_NLos=[2]
    x_threshold = [5]
    model = Sequential()
    model.add(Dense(hidden_size, input_shape=(2, ), activation='relu'))
    model.add(Dense(hidden_size, activation='relu'))
    model.add(Dense(num_actions))
    model.compile(sgd(lr=.2), "mse")
    # Define environment/game
    env = FooEnv(f_c, eta_Los, eta_NLos)
    # Initialize experience replay object
    exp_replay = ExperienceReplay(max_memory=max_memory)
    FooEnv.reset()

And this is my FooEnv code:

import numpy as  np
import math
class FooEnv(object):
  def __init__(self, f_c, eta_Los, eta_NLos):
    self.f_c = f_c
    self.eta_Los = eta_Los
    self.eta_NLos = eta_NLos
    self.num_actions = 2
  def reset(self):
    state=self.state
    E_Consumtion, Average_Delay_UAV, Average_DeLay_FAP = state
    E_Consumtion=0
    Average_Delay_UAV=0
    Average_DeLay_FAP=0
    self.state = np.append(E_Consumtion,Average_Delay_UAV,Average_DeLay_FAP)
    self.steps_beyond_done = None
    return np.array(self.state)

I would greatly appreciated it if you could help me with this.

Answer 1

FooEnv is a class and env is an object of that class. You want to reset the object, not the class.