add cartpole DQN

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# Cartpole DQN

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Deep Q-Learning Network with Keras and OpenAI Gym, based on [Keon Kim's code](https://github.com/keon/deep-q-learning/blob/master/dqn.py).

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#### Import dependencies

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``` python

import random

import gym

import numpy as np

from collections import deque

from keras.models import Sequential

from keras.layers import Dense

from keras.optimizers import Adam

import os

```

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#### Set parameters

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``` python

env = # FILL IN

```

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``` python

state_size = env.observation_space.shape[0]

state_size

```

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``` python

action_size = env.action_space.n

action_size

```

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``` python

batch_size = # FILL IN

```

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``` python

n_episodes = # FILL IN

```

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``` python

output_dir = 'model_output/cartpole/'

```

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``` python

if not os.path.exists(output_dir):

    os.makedirs(output_dir)

```

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#### Define agent

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``` python

class DQNAgent:

    def __init__(self, state_size, action_size):

        self.state_size = state_size

        self.action_size = action_size

        self.memory = deque(maxlen=2000)

        self.gamma = # FILL IN

        self.epsilon = # FILL IN

        self.epsilon_decay = # FILL IN

        self.epsilon_min = # FILL IN

        self.learning_rate = # FILL IN

        self.model = self._build_model()

    def _build_model(self):

        model = Sequential()

        # FILL IN NEURAL NETWORK ARCHITECTURE

        # COMPILE MODEL

        return model

    def remember(self, state, action, reward, next_state, done):

        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):

        if np.random.rand() <= self.epsilon:

            return random.randrange(self.action_size)

        act_values = self.model.predict(state)

        return np.argmax(act_values[0])

    def replay(self, batch_size):

        minibatch = random.sample(self.memory, batch_size)

        for state, action, reward, next_state, done in minibatch:

            target = reward # N.B.: if done

            if not done:

                target = (reward + self.gamma * np.amax(self.model.predict(next_state)[0])) # (maximum target Q based on future action a')

            target_f = self.model.predict(state)

            target_f[0][action] = target

            self.model.fit(state, target_f, epochs=1, verbose=0)

        if self.epsilon > self.epsilon_min:

            self.epsilon *= self.epsilon_decay

    def load(self, name):

        self.model.load_weights(name)

    def save(self, name):

        self.model.save_weights(name)

```

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#### Interact with environment

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``` python

agent = DQNAgent(state_size, action_size)

```

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``` python

done = False

for e in range(n_episodes):

    state = env.reset()

    state = np.reshape(state, [1, state_size])

    for time in range(5000):

        #  env.render()

        action = agent.act(state)

        next_state, reward, done, _ = env.step(action)

        reward = reward if not done else -10

        next_state = np.reshape(next_state, [1, state_size])

        agent.remember(state, action, reward, next_state, done)

        state = next_state

        if done:

            print("episode: {}/{}, score: {}, e: {:.2}".format(e, n_episodes, time, agent.epsilon))

            break

    if len(agent.memory) > batch_size:

        agent.replay(batch_size)

    if e % 50 == 0:

        agent.save(output_dir + "weights_" + '{:04d}'.format(e) + ".hdf5")

```

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``` python

# saved agents can be loaded with agent.load("./path/filename.hdf5")

```