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- 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
- EPISODES = 2000
- 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
- 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))
- 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]) # returns action
- def replay(self, batch_size):
- minibatch = random.sample(self.memory, batch_size)
- for state, action, reward, next_state, done in minibatch:
- target = reward
- if not done:
- target = (reward + self.gamma *
- np.amax(self.model.predict(next_state)[0]))
- 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):
- print('load weight')
- self.model.load_weights(name)
- def save(self, name):
- print('save weight')
- self.model.save_weights(name)
- if __name__ == "__main__":
- env = gym.make('CartPole-v0')
- state_size = env.observation_space.shape[0]
- action_size = env.action_space.n
- print('state_size', state_size)
- print('action_size', action_size)
- agent = DQNAgent(state_size, action_size)
- #agent.load("./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()
- 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, EPISODES, time, agent.epsilon))
- break
- if len(agent.memory) > batch_size:
- agent.replay(batch_size)
- #if e % 50 == 0:
- # agent.save("./cartpole-dqn.h5")
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