Deep_Q_Network.py

import random
import gym
import numpy as np

from collections import deque # double ended queue for experience memory

from tensorflow.contrib.keras.python.keras.models import Sequential
from tensorflow.contrib.keras.python.keras.layers import Dense
from tensorflow.contrib.keras.python.keras.optimizers import Adam

EPISODES = 1000


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 = 0.95    # discount rate
        self.epsilon = 1.0  # exploration rate
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
        self.model = self._build_model()

    def _build_model(self):
        # Neural Net for Deep-Q learning Model--->>  #Q=NN.predict(state)
        model = Sequential()
        model.add(Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse',
                      optimizer=Adam(lr=self.learning_rate))

        return model

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


    #get action
    def act(self, state):

        #select random action with prob=epsilon else action=maxQ
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0])  # returns action

    #experience replay
    def replay(self, batch_size):

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

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

            if not done:
                #calculate target for each minibatch
                Q_next=self.model.predict(next_state)[0]
                target = (reward + self.gamma *np.amax(Q_next))#Belman

            target_f = self.model.predict(state)
            target_f[0][action] = target

            #train network
            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)



env = gym.make('CartPole-v1')
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
agent = DQNAgent(state_size, action_size)
# agent.load("./save/cartpole-dqn.h5")
done = False
batch_size = 32

for e in range(EPISODES):

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

    for time in range(500):

        # env.render()

        #e-greedy action
        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])

        #add to experience memory
        agent.remember(state, action, reward, next_state, done)

        state = next_state

        if done:
            print("episode: {}/{}, score: {}, e: {:.2}"
                  .format(e, EPISODES, time, agent.epsilon))
            break

    #experience replay
    if len(agent.memory) > batch_size:
        agent.replay(batch_size)

    # if e % 10 == 0:
        #     agent.save("./save/cartpole-dqn.h5")