tf.zeros(action_dim)

`tf.zeros(action_dim)` 是 TensorFlow 的一个函数，用于创建一个指定形状的全零张量。其中，`action_dim` 表示张量的形状，通常是一个整数列表或元组，用于指定每个维度的大小。

具体来说，如果我们想要创建一个形状为 `(batch_size, action_dim)` 的全零张量，可以使用以下代码：

import tensorflow as tf

batch_size = 32
action_dim = 5

# 创建一个形状为 (batch_size, action_dim) 的全零张量
zeros_tensor = tf.zeros((batch_size, action_dim))

这里的 `zeros_tensor` 就是一个形状为 `(batch_size, action_dim)` 的全零张量。我们可以将其作为模型的输出或者中间变量，用于进行后续的计算或者优化。

相关问题

soft actor critic tensorflow1.x code

以下是一个使用TensorFlow 1.x实现Soft Actor-Critic（SAC）算法的代码示例：

import tensorflow as tf
import numpy as np
import gym

# Hyperparameters
gamma = 0.99
tau = 0.005
hidden_sizes = [256, 256]
batch_size = 100
buffer_size = 10000
learning_rate = 0.0003
alpha = 0.2
target_entropy = -1.0

# Create the replay buffer
class ReplayBuffer:
    def __init__(self, obs_dim, act_dim, size):
        self.obs_buf = np.zeros((size, obs_dim), dtype=np.float32)
        self.act_buf = np.zeros((size, act_dim), dtype=np.float32)
        self.rew_buf = np.zeros(size, dtype=np.float32)
        self.next_obs_buf = np.zeros((size, obs_dim), dtype=np.float32)
        self.done_buf = np.zeros(size, dtype=np.float32)
        self.ptr, self.size, self.max_size = 0, 0, size

    def store(self, obs, act, rew, next_obs, done):
        self.obs_buf[self.ptr] = obs
        self.act_buf[self.ptr] = act
        self.rew_buf[self.ptr] = rew
        self.next_obs_buf[self.ptr] = next_obs
        self.done_buf[self.ptr] = done
        self.ptr = (self.ptr+1) % self.max_size
        self.size = min(self.size+1, self.max_size)

    def sample_batch(self, batch_size=batch_size):
        idxs = np.random.randint(0, self.size, size=batch_size)
        return dict(obs=self.obs_buf[idxs],
                    act=self.act_buf[idxs],
                    rew=self.rew_buf[idxs],
                    next_obs=self.next_obs_buf[idxs],
                    done=self.done_buf[idxs])

# Create the actor and critic networks
class MLP(tf.keras.Model):
    def __init__(self, sizes, activation=tf.nn.relu, output_activation=None):
        super(MLP, self).__init__()
        self.layers_ = []
        for i, size in enumerate(sizes[:-1]):
            layer = tf.keras.layers.Dense(units=size, activation=activation)
            self.layers_.append(layer)
        self.layers_.append(tf.keras.layers.Dense(units=sizes[-1], activation=output_activation))

    def call(self, inputs):
        x = inputs
        for layer in self.layers_:
            x = layer(x)
        return x

class ActorCritic(tf.keras.Model):
    def __init__(self, obs_dim, act_dim, hidden_sizes, activation=tf.nn.relu, output_activation=None):
        super(ActorCritic, self).__init__()
        self.q1 = MLP(hidden_sizes + [1], activation, output_activation)
        self.q2 = MLP(hidden_sizes + [1], activation, output_activation)
        self.v = MLP(hidden_sizes + [1], activation, output_activation)
        self.pi = MLP(hidden_sizes + [act_dim], activation, tf.nn.tanh)

    def call(self, obs, act=None):
        q1 = self.q1(tf.concat([obs, act], axis=-1))
        q2 = self.q2(tf.concat([obs, act], axis=-1))
        v = self.v(obs)
        pi = self.pi(obs)
        return q1, q2, v, pi

    def act(self, obs):
        pi = self.pi(obs)
        return pi.numpy()

# Create the SAC agent
class SAC:
    def __init__(self, obs_dim, act_dim, hidden_sizes, buffer_size, batch_size, learning_rate, alpha, gamma, tau, target_entropy):
        self.q_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
        self.v_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
        self.pi_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
        self.replay_buffer = ReplayBuffer(obs_dim=obs_dim, act_dim=act_dim, size=buffer_size)
        self.batch_size = batch_size
        self.alpha = alpha
        self.gamma = gamma
        self.tau = tau
        self.target_entropy = target_entropy
        self.obs_dim = obs_dim
        self.act_dim = act_dim
        self.hidden_sizes = hidden_sizes
        self.actor_critic = ActorCritic(obs_dim, act_dim, hidden_sizes)

    def update(self, data):
        obs = data['obs']
        act = data['act']
        rew = data['rew']
        next_obs = data['next_obs']
        done = data['done']

        with tf.GradientTape(persistent=True) as tape:
            q1, q2, v, pi = self.actor_critic(obs, act)
            _, _, _, next_pi = self.actor_critic(next_obs)

            v_target = self.target_v(next_obs, next_pi)
            q_target = rew + self.gamma * (1 - done) * v_target

            q1_loss = tf.reduce_mean(tf.square(q1 - q_target))
            q2_loss = tf.reduce_mean(tf.square(q2 - q_target))
            v_loss = tf.reduce_mean(tf.square(v - v_target))

            pi_loss = tf.reduce_mean(self.alpha * pi.log_prob(act) - q1)

            alpha_loss = tf.reduce_mean(-self.alpha * (self.target_entropy - pi.entropy()))

        q1_grads = tape.gradient(q1_loss, self.actor_critic.q1.trainable_variables)
        self.q_optimizer.apply_gradients(zip(q1_grads, self.actor_critic.q1.trainable_variables))

        q2_grads = tape.gradient(q2_loss, self.actor_critic.q2.trainable_variables)
        self.q_optimizer.apply_gradients(zip(q2_grads, self.actor_critic.q2.trainable_variables))

        v_grads = tape.gradient(v_loss, self.actor_critic.v.trainable_variables)
        self.v_optimizer.apply_gradients(zip(v_grads, self.actor_critic.v.trainable_variables))

        pi_grads = tape.gradient(pi_loss, self.actor_critic.pi.trainable_variables)
        self.pi_optimizer.apply_gradients(zip(pi_grads, self.actor_critic.pi.trainable_variables))

        alpha_grads = tape.gradient(alpha_loss, [self.alpha])
        self.alpha = tf.clip_by_value(self.alpha - learning_rate * alpha_grads[0], 0, 1)

        del tape

    def target_v(self, obs, pi):
        q1, q2, _, _ = self.actor_critic(obs, pi)
        q = tf.minimum(q1, q2)
        v_target = tf.reduce_mean(q - self.alpha * pi.entropy())
        return v_target

    def train(self):
        data = self.replay_buffer.sample_batch(self.batch_size)
        self.update(data)

    def store(self, obs, act, rew, next_obs, done):
        self.replay_buffer.store(obs, act, rew, next_obs, done)

    def act(self, obs):
        return self.actor_critic.act(obs)

    def save(self, save_path):
        self.actor_critic.save_weights(save_path)

    def load(self, load_path):
        self.actor_critic.load_weights(load_path)

# Train the SAC agent on the gym environment
env = gym.make('Pendulum-v0')
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
sac = SAC(obs_dim, act_dim, hidden_sizes, buffer_size, batch_size, learning_rate, alpha, gamma, tau, target_entropy)

for i in range(1000):
    obs = env.reset()
    total_reward = 0
    done = False

    while not done:
        act = sac.act(obs.reshape(1, -1))
        next_obs, rew, done, _ = env.step(act[0])

        sac.store(obs, act, rew, next_obs, done)
        sac.train()

        obs = next_obs
        total_reward += rew

    print('Epoch: {}, Total Reward: {:.2f}'.format(i, total_reward))

sac.save('sac_model')

请注意，这只是一个基本的实现示例，其中有许多可以进行改进和优化的方面。

给我TRPO解决BipedalWalkerHardcore_v3的代码

TRPO（Trust Region Policy Optimization）是一种用于强化学习的优化算法，用于更新策略参数。下面是使用TRPO解决BipedalWalkerHardcore_v3的Python代码示例：

import gym
import numpy as np
import tensorflow as tf
from scipy import optimize

env = gym.make('BipedalWalkerHardcore-v3')

# 策略网络
class PolicyNet:
    def __init__(self, state_dim, action_dim, hidden_size):
        self.state = tf.placeholder(tf.float32, [None, state_dim])
        l1 = tf.layers.dense(self.state, hidden_size, tf.nn.relu)
        l2 = tf.layers.dense(l1, hidden_size, tf.nn.relu)
        self.action_mean = tf.layers.dense(l2, action_dim, tf.nn.tanh)
        self.action_std = tf.Variable(1.0, trainable=True)

        self.action = tf.placeholder(tf.float32, [None, action_dim])
        self.advantage = tf.placeholder(tf.float32, [None])

        normal_dist = tf.distributions.Normal(self.action_mean, self.action_std)
        log_prob = normal_dist.log_prob(self.action)
        loss = -tf.reduce_mean(log_prob * self.advantage)
        kl = tf.distributions.kl_divergence(normal_dist, normal_dist)
        self.kl_mean = tf.reduce_mean(kl)

        self.train_op = self._create_train_op(loss)

    def _create_train_op(self, loss):
        optimizer = tf.train.AdamOptimizer()
        grads_and_vars = optimizer.compute_gradients(loss)
        flat_grads = tf.concat([tf.reshape(g, [-1]) for g, _ in grads_and_vars], axis=0)
        var_shapes = [tf.reshape(v, [-1]).shape for _, v in grads_and_vars]
        var_sizes = [np.prod(s) for s in var_shapes]
        cum_sizes = np.cumsum([0] + var_sizes)
        flat_params = tf.concat([tf.reshape(v, [-1]) for _, v in grads_and_vars], axis=0)
        kl_grads = tf.gradients(self.kl_mean, grads_and_vars)
        kl_grads = [tf.reshape(g, [-1]) / tf.cast(tf.reduce_prod(s), tf.float32) for g, (s, _) in zip(kl_grads, var_shapes)]
        kl_grad = tf.concat(kl_grads, axis=0)
        grad_kl_grad = tf.reduce_sum(flat_grads * kl_grad)
        hessian_vector_product = tf.gradients(grad_kl_grad, flat_params)
        hessian_vector_product = tf.concat(hessian_vector_product, axis=0)
        grads_and_hvp = list(zip(hessian_vector_product, flat_params))
        flat_grad_hvp = tf.concat([tf.reshape(g, [-1]) for g, _ in grads_and_hvp], axis=0)
        fisher_vector_product = flat_grad_hvp + 0.1 * flat_params
        gradient = tf.stop_gradient(fisher_vector_product)
        learning_rate = tf.sqrt(0.01 / tf.norm(gradient))
        clipped_gradient = tf.clip_by_norm(gradient, 0.5)
        train_op = tf.assign_sub(flat_params, learning_rate * clipped_gradient)
        train_op = tf.group(*[tf.assign(v, p) for (v, _), p in zip(grads_and_vars, tf.split(flat_params, cum_sizes[1:-1]))])
        return train_op

    def get_action(self, state):
        return self.action_mean.eval(feed_dict={self.state: state.reshape(1, -1)})[0]

    def get_kl(self, state, action):
        return self.kl_mean.eval(feed_dict={self.state: state, self.action: action})

    def train(self, state, action, advantage):
        feed_dict = {self.state: state, self.action: action, self.advantage: advantage}
        self.train_op.run(feed_dict=feed_dict)

# 值网络
class ValueNet:
    def __init__(self, state_dim, hidden_size):
        self.state = tf.placeholder(tf.float32, [None, state_dim])
        l1 = tf.layers.dense(self.state, hidden_size, tf.nn.relu)
        l2 = tf.layers.dense(l1, hidden_size, tf.nn.relu)
        self.value = tf.layers.dense(l2, 1)

        self.target_value = tf.placeholder(tf.float32, [None])
        loss = tf.reduce_mean(tf.square(self.value - self.target_value))
        self.train_op = tf.train.AdamOptimizer().minimize(loss)

    def get_value(self, state):
        return self.value.eval(feed_dict={self.state: state.reshape(1, -1)})[0, 0]

    def train(self, state, target_value):
        feed_dict = {self.state: state, self.target_value: target_value}
        self.train_op.run(feed_dict=feed_dict)

# 训练
def train():
    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.shape[0]
    hidden_size = 64

    policy_net = PolicyNet(state_dim, action_dim, hidden_size)
    value_net = ValueNet(state_dim, hidden_size)

    gamma = 0.99
    lam = 0.95
    batch_size = 2048
    max_step = 1000000
    render = False

    state = env.reset()
    for step in range(max_step):
        states = []
        actions = []
        rewards = []
        values = []

        for _ in range(batch_size):
            action = policy_net.get_action(state)
            next_state, reward, done, _ = env.step(action)
            states.append(state)
            actions.append(action)
            rewards.append(reward)
            if done:
                values.append(0)
                state = env.reset()
            else:
                values.append(value_net.get_value(next_state))
                state = next_state
            if render:
                env.render()
        values = np.array(values)

        returns = np.zeros_like(rewards)
        advantages = np.zeros_like(rewards)
        last_return = 0
        last_value = 0
        last_advantage = 0
        for t in reversed(range(batch_size)):
            returns[t] = rewards[t] + gamma * last_return
            delta = rewards[t] + gamma * last_value - values[t]
            advantages[t] = delta + gamma * lam * last_advantage
            last_return = returns[t]
            last_value = values[t]
            last_advantage = advantages[t]

        advantages = (advantages - np.mean(advantages)) / np.std(advantages)
        policy_net.train(np.array(states), np.array(actions), advantages)
        value_net.train(np.array(states), returns)

        if step % 100 == 0:
            print('step=%d, reward=%f' % (step, np.mean(rewards)))
        if np.mean(rewards) > 300:
            render = True

train()

这段代码使用TensorFlow实现了一个策略网络和一个值网络，使用TRPO算法更新策略参数和值函数参数。在训练过程中，首先采集一定数量的数据，然后计算每个状态的回报和优势，并使用这些数据来更新策略网络和值网络。在每一步训练之后，打印出当前的平均回报。当平均回报超过300时，开始渲染环境。