Deep Reinforcent Learning Model, experience replay

Question

So I am working on a deep RL model in OpenAI Gym. I have everything else working except for my function that allows my agent experience replay.

here is the code:

import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense
from collections import deque
from keras.optimizers import Adam
import numpy as np
import gym
import tensorflow as tf
import math
import random



env = gym.make('MountainCar-v0')

#hyperparams
gamma = .99 #discount factor for reward
alpha = .01 #learning rate
beta  = .01 #decay
batch_size = 32
memory = deque(maxlen=2000)
epsilon = 1.0


#building model
model = Sequential()
model.add(Dense(12, activation='sigmoid', input_dim=2))
model.add(Dense(12, activation='tanh'))
model.add(Dense(2, activation='linear'))
model.compile(loss='mse', optimizer=Adam(lr=alpha, decay=beta))


def preprocess_state(state):
    return np.reshape(state, [1, 2])

def experience(state, action, reward, next_state, done):
    # memory for replay
    memory.append((state, action, reward, next_state, done))

class agent():




def choose_action(self, state):
    rand_number = np.random.randint(2)
# choosing random action 50%, acting greedily with respect to value func/policy (naive e-greedy)
    return env.action_space.sample() if (np.random.random() <= epsilon) else np.argmax(model.predict(state))

def replay(self):
    x_batch, y_batch = [], []
    minibatch = random.sample(memory, batch_size)
    for state, action, reward, next_state, done in minibatch:
        y_target = model.predict(state)
        y_target[0][action] = reward if done else reward + gamma * np.max(model.predict(next_state)[0])
        x_batch.append(state[0])
        y_batch.append(y_target[0])


agent = agent()


def run():

for episodes in range(1000):
    state = preprocess_state(env.reset())
    tot_reward = 0
    done = False
    while not done:
        env.render()
        action = agent.choose_action(state)
        next_state, reward, done, t = env.step(action)
        next_state = preprocess_state(next_state)
        #record results

        experience(state,action,reward,next_state,done)

        tot_reward += reward

        #replay for agent
        if batch_size < len(memory):
            agent.replay()

        print("episode: {} | score: {} |".format(
       episodes,tot_reward))

        if done:
            print "You did it"
            break

run()

The issue is with this line:

  y_target[0][action] = reward if done else reward + gamma * np.max(model.predict(next_state)[0])

and the error: