强化学习在生物信息学领域的应用

    pip install gym
    pip install tensorflow
    
      
    
    代码解读

    import gym
    from gym import spaces
    
    class ProteinDesignEnv(gym.Env):
    def __init__(self):
        super(ProteinDesignEnv, self).__init__()
        self.action_space = spaces.Discrete(20)  # 20种氨基酸
        self.observation_space = spaces.Box(low=0, high=1, shape=(100, 20))  # 最多100个氨基酸，每个氨基酸用一个20维的向量表示
    
    def step(self, action):
        # 添加一个氨基酸
        self.sequence.append(action)
        # 计算奖励
        reward = self.evaluate(self.sequence)
        return self.sequence, reward, len(self.sequence) == 100, {}
    
    def reset(self):
        self.sequence = []
        return self.sequence
    
    def evaluate(self, sequence):
        # 这里只是一个示例，实际的评估函数应该根据蛋白质的稳定性或活性来计算
        return len(sequence)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import tensorflow as tf
    from tensorflow.keras import layers
    
    class DQN(tf.keras.Model):
    def __init__(self, action_size):
        super(DQN, self).__init__()
        self.dense1 = layers.Dense(128, activation='relu')
        self.dense2 = layers.Dense(128, activation='relu')
        self.dense3 = layers.Dense(action_size)
    
    def call(self, x):
        x = self.dense1(x)
        x = self.dense2(x)
        return self.dense3(x)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import numpy as np
    from tensorflow.keras.optimizers import Adam
    from collections import deque
    import random
    
    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  # 折扣因子
        self.epsilon = 1.0  # 探索率
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.model = DQN(action_size)
        self.model.compile(loss='mse', optimizer=Adam())
    
    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 = self.model.predict(state)
            if done:
                target[0][action] = reward
            else:
                Q_future = max(self.model.predict(next_state)[0])
                target[0][action] = reward + Q_future * self.gamma
            self.model.fit(state, target, 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 = ProteinDesignEnv()
    agent = DQNAgent(env.observation_space.shape[0], env.action_space.n)
    batch_size = 32
    
    for e in range(1000):
    state = env.reset()
    state = np.reshape(state, [1, state_size])
    for time in range(500):
        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, 1000, time, agent.epsilon))
            break
        if len(agent.memory) > batch_size:
            agent.replay(batch_size)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读