DQN 倒立摆 简单程序

好的，以下是使用 DQN（Deep Q-Network）算法来解决倒立摆问题的示例程序，使用 Python 和 TensorFlow 库： ```python import gym import tensorflow as tf import numpy as np # 定义神经网络模型 class DQN: def __init__(self, env): self.input_size = env.observation_space.shape[0] self.output_size = env.action_space.n self.hidden_size = 32 self.learning_rate = 0.1 self.X = tf.placeholder(tf.float32, [None, self.input_size], name="input_x") W1 = tf.get_variable("W1", shape=[self.input_size, self.hidden_size], initializer=tf.contrib.layers.xavier_initializer()) layer1 = tf.nn.relu(tf.matmul(self.X, W1)) W2 = tf.get_variable("W2", shape=[self.hidden_size, self.output_size], initializer=tf.contrib.layers.xavier_initializer()) self.Qpred = tf.matmul(layer1, W2) self.Y = tf.placeholder(shape=[None, self.output_size], dtype=tf.float32) self.loss = tf.reduce_mean(tf.square(self.Y - self.Qpred)) self.train = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.loss) # 预测动作 def predict(self, state, sess): x = np.reshape(state, [1, self.input_size]) return sess.run(self.Qpred, feed_dict={self.X: x}) # 更新神经网络 def update(self, x_stack, y_stack, sess): sess.run(self.train, feed_dict={self.X: x_stack, self.Y: y_stack}) # DQN算法 def DQN_algorithm(): env = gym.make('CartPole-v0') input_size = env.observation_space.shape[0] output_size = env.action_space.n max_episodes = 5000 dis = 0.99 replay_buffer = [] batch_size = 10 step_history = [] step_limit = 10000 e = 1.0 / ((max_episodes / 10) + 1) pre_states = np.zeros(input_size) with tf.Session() as sess: DQNetwork = DQN(env) tf.global_variables_initializer().run() for episode in range(max_episodes): e = 1. / ((episode / 10) + 1) done = False state = env.reset() step_count = 0 while not done: if np.random.rand(1) < e: action = env.action_space.sample() else: action = np.argmax(DQNetwork.predict(state, sess)) next_state, reward, done, _ = env.step(action) if done: reward = -100 replay_buffer.append([state, action, reward, next_state, done]) if len(replay_buffer) > 50000: replay_buffer.pop(0) state = next_state step_count += 1 if step_count > step_limit: break print("Episode: {} steps: {}".format(episode, step_count)) step_history.append(step_count) if len(replay_buffer) > batch_size: minibatch = np.array(replay_buffer)[-batch_size:] states = np.vstack(minibatch[:, 0]) actions = minibatch[:, 1] rewards = minibatch[:, 2] next_states = np.vstack(minibatch[:, 3]) done = minibatch[:, 4] Q_targets = rewards + dis * np.max(DQNetwork.predict(next_states, sess), axis=1) * ~done Q_targets = np.reshape(Q_targets, [batch_size, output_size]) DQNetwork.update(states, Q_targets, sess) pre_states = state env.close() return step_history ``` 这个程序将使用 DQN 算法来训练一个神经网络，以解决 OpenAI Gym 中的倒立摆问题。程序将输出每个 episode 的步数，并将步数存储在列表 `step_history` 中。您可以将 `step_history` 绘制成图表，以查看算法的性能如何随时间变化。