PARL 强化学习框架学习

最近参加了百度的的PARL深度强化学习课程，算是对强化学习有了一定了解，因为之前并没有学习过强化学习相关的知识，粗略入门，体验了PARL框架，确实对新手比较友好。

入门学习了比较基础的算法，如SARSA，Q-Learning，DQN，PG，DDPG。

能在AI studio上直接跑模型，直接感受模型的效果，对入门学习比较有帮助。

强化学习主要了解3个概念，agent，model，algorithm

下面是代码实例，以DQN为例

model

class Model(parl.Model):
    def __init__(self, act_dim):
        hid1_size = 128
        hid2_size = 128
        # 3层全连接网络
        self.fc1 = layers.fc(size=hid1_size, act='relu')
        self.fc2 = layers.fc(size=hid2_size, act='relu')
        self.fc3 = layers.fc(size=act_dim, act=None)

    def value(self, obs):
        # 定义网络
        # 输入state，输出所有action对应的Q，[Q(s,a1), Q(s,a2), Q(s,a3)...]
        h1 = self.fc1(obs)
        h2 = self.fc2(h1)
        Q = self.fc3(h2)
        return Q

algorithm

# from parl.algorithms import DQN # 也可以直接从parl库中导入DQN算法

class DQN(parl.Algorithm):
    def __init__(self, model, act_dim=None, gamma=None, lr=None):
        """ DQN algorithm
        
        Args:
            model (parl.Model): 定义Q函数的前向网络结构
            act_dim (int): action空间的维度，即有几个action
            gamma (float): reward的衰减因子
            lr (float): learning rate 学习率.
        """
        self.model = model
        self.target_model = copy.deepcopy(model)

        assert isinstance(act_dim, int)
        assert isinstance(gamma, float)
        assert isinstance(lr, float)
        self.act_dim = act_dim
        self.gamma = gamma
        self.lr = lr

    def predict(self, obs):
        """ 使用self.model的value网络来获取 [Q(s,a1),Q(s,a2),...]
        """
        return self.model.value(obs)

    def learn(self, obs, action, reward, next_obs, terminal):
        """ 使用DQN算法更新self.model的value网络
        """
        # 从target_model中获取 max Q' 的值，用于计算target_Q
        next_pred_value = self.target_model.value(next_obs)
        best_v = layers.reduce_max(next_pred_value, dim=1)
        best_v.stop_gradient = True  # 阻止梯度传递
        terminal = layers.cast(terminal, dtype='float32')
        target = reward + (1.0 - terminal) * self.gamma * best_v

        pred_value = self.model.value(obs)  # 获取Q预测值
        # 将action转onehot向量，比如：3 => [0,0,0,1,0]
        action_onehot = layers.one_hot(action, self.act_dim)
        action_onehot = layers.cast(action_onehot, dtype='float32')
        # 下面一行是逐元素相乘，拿到action对应的 Q(s,a)
        # 比如：pred_value = [[2.3, 5.7, 1.2, 3.9, 1.4]], action_onehot = [[0,0,0,1,0]]
        #  ==> pred_action_value = [[3.9]]
        pred_action_value = layers.reduce_sum(
            layers.elementwise_mul(action_onehot, pred_value), dim=1)

        # 计算 Q(s,a) 与 target_Q的均方差，得到loss
        cost = layers.square_error_cost(pred_action_value, target)
        cost = layers.reduce_mean(cost)
        optimizer = fluid.optimizer.Adam(learning_rate=self.lr)  # 使用Adam优化器
        optimizer.minimize(cost)
        return cost

    def sync_target(self):
        """ 把 self.model 的模型参数值同步到 self.target_model
        """
        self.model.sync_weights_to(self.target_model)

agent

class Agent(parl.Agent):
    def __init__(self,
                 algorithm,
                 obs_dim,
                 act_dim,
                 e_greed=0.1,
                 e_greed_decrement=0):
        assert isinstance(obs_dim, int)
        assert isinstance(act_dim, int)
        self.obs_dim = obs_dim
        self.act_dim = act_dim
        super(Agent, self).__init__(algorithm)

        self.global_step = 0
        self.update_target_steps = 200  # 每隔200个training steps再把model的参数复制到target_model中

        self.e_greed = e_greed  # 有一定概率随机选取动作，探索
        self.e_greed_decrement = e_greed_decrement  # 随着训练逐步收敛，探索的程度慢慢降低

    def build_program(self):
        self.pred_program = fluid.Program()
        self.learn_program = fluid.Program()

        with fluid.program_guard(self.pred_program):  # 搭建计算图用于 预测动作，定义输入输出变量
            obs = layers.data(
                name='obs', shape=[self.obs_dim], dtype='float32')
            self.value = self.alg.predict(obs)

        with fluid.program_guard(self.learn_program):  # 搭建计算图用于 更新Q网络，定义输入输出变量
            obs = layers.data(
                name='obs', shape=[self.obs_dim], dtype='float32')
            action = layers.data(name='act', shape=[1], dtype='int32')
            reward = layers.data(name='reward', shape=[], dtype='float32')
            next_obs = layers.data(
                name='next_obs', shape=[self.obs_dim], dtype='float32')
            terminal = layers.data(name='terminal', shape=[], dtype='bool')
            self.cost = self.alg.learn(obs, action, reward, next_obs, terminal)

    def sample(self, obs):
        sample = np.random.rand()  # 产生0~1之间的小数
        if sample < self.e_greed:
            act = np.random.randint(self.act_dim)  # 探索：每个动作都有概率被选择
        else:
            act = self.predict(obs)  # 选择最优动作
        self.e_greed = max(
            0.01, self.e_greed - self.e_greed_decrement)  # 随着训练逐步收敛，探索的程度慢慢降低
        return act

    def predict(self, obs):  # 选择最优动作
        obs = np.expand_dims(obs, axis=0)
        pred_Q = self.fluid_executor.run(
            self.pred_program,
            feed={'obs': obs.astype('float32')},
            fetch_list=[self.value])[0]
        pred_Q = np.squeeze(pred_Q, axis=0)
        act = np.argmax(pred_Q)  # 选择Q最大的下标，即对应的动作
        return act

    def learn(self, obs, act, reward, next_obs, terminal):
        # 每隔200个training steps同步一次model和target_model的参数
        if self.global_step % self.update_target_steps == 0:
            self.alg.sync_target()
        self.global_step += 1

        act = np.expand_dims(act, -1)
        feed = {
            'obs': obs.astype('float32'),
            'act': act.astype('int32'),
            'reward': reward,
            'next_obs': next_obs.astype('float32'),
            'terminal': terminal
        }
        cost = self.fluid_executor.run(
            self.learn_program, feed=feed, fetch_list=[self.cost])[0]  # 训练一次网络
        return cost