强化学习中的Actor算法中的exp_v是什么
时间: 2024-06-07 19:05:33 浏览: 7
在强化学习中,Actor算法是一种基于策略梯度的算法,用于学习连续动作控制问题。exp_v是Actor算法中的一个重要参数,表示Actor的期望价值函数。这个期望价值函数用于计算当前状态下不同动作的价值,从而指导Actor选择最优的动作。
在Actor算法中,exp_v是通过蒙特卡罗方法估计的,即通过模拟多次状态转移过程,计算每个动作的回报值的平均值。这个平均值就是exp_v,它可以用来更新Actor的策略参数,以达到更优的控制效果。
需要注意的是,exp_v的计算是非常耗时的,因为需要进行多次模拟,所以在实际应用中需要进行优化,例如使用函数逼近等方法来加速计算。
相关问题
请采用python基于Tensorflow架构提供包括1个actor网络,2个V Critic网络(1个V Critic网络,1个Target V Critic网络),2个Q Critic网络的基于最大熵的SAC版本的强化学习算法代码
好的,我可以为您提供一个基于TensorFlow框架的最大熵SAC算法的代码框架,其中包括1个Actor网络、2个V Critic网络(1个V Critic网络,1个Target V Critic网络)和2个Q Critic网络。
```
import tensorflow as tf
import numpy as np
import tensorflow_probability as tfp
tfd = tfp.distributions
class Actor(tf.keras.Model):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.layer1 = tf.keras.layers.Dense(256, activation='relu')
self.layer2 = tf.keras.layers.Dense(256, activation='relu')
self.mean = tf.keras.layers.Dense(action_dim, activation='tanh')
self.log_std = tf.keras.layers.Dense(action_dim, activation='tanh')
self.max_action = max_action
def call(self, state):
x = self.layer1(state)
x = self.layer2(x)
mean = self.mean(x) * self.max_action
log_std = self.log_std(x)
log_std = tf.clip_by_value(log_std, -20, 2)
std = tf.exp(log_std)
dist = tfd.Normal(mean, std)
action = dist.sample()
log_prob = dist.log_prob(action)
log_prob -= tf.reduce_sum(2 * (np.log(2) - action - tf.nn.softplus(-2 * action)), axis=1, keepdims=True)
action = tf.tanh(action)
return action, log_prob
class V_Critic(tf.keras.Model):
def __init__(self, state_dim):
super(V_Critic, self).__init__()
self.layer1 = tf.keras.layers.Dense(256, activation='relu')
self.layer2 = tf.keras.layers.Dense(256, activation='relu')
self.layer3 = tf.keras.layers.Dense(1)
def call(self, state):
x = self.layer1(state)
x = self.layer2(x)
value = self.layer3(x)
return value
class Q_Critic(tf.keras.Model):
def __init__(self, state_dim, action_dim):
super(Q_Critic, self).__init__()
self.layer1 = tf.keras.layers.Dense(256, activation='relu')
self.layer2 = tf.keras.layers.Dense(256, activation='relu')
self.layer3 = tf.keras.layers.Dense(1)
def call(self, state, action):
state_action = tf.concat([state, action], axis=1)
x = self.layer1(state_action)
x = self.layer2(x)
q_value = self.layer3(x)
return q_value
class SAC():
def __init__(self, state_dim, action_dim, max_action):
self.actor = Actor(state_dim, action_dim, max_action)
self.v_critic = V_Critic(state_dim)
self.target_v_critic = V_Critic(state_dim)
self.q1_critic = Q_Critic(state_dim, action_dim)
self.q2_critic = Q_Critic(state_dim, action_dim)
self.target_q1_critic = Q_Critic(state_dim, action_dim)
self.target_q2_critic = Q_Critic(state_dim, action_dim)
self.actor_optim = tf.keras.optimizers.Adam()
self.v_critic_optim = tf.keras.optimizers.Adam()
self.q1_critic_optim = tf.keras.optimizers.Adam()
self.q2_critic_optim = tf.keras.optimizers.Adam()
self.max_action = max_action
def select_action(self, state):
state = tf.expand_dims(tf.convert_to_tensor(state), axis=0)
action, _ = self.actor(state)
return action.numpy()[0]
def train(self, replay_buffer, batch_size=100, gamma=0.99, tau=0.005, alpha=0.2):
state, action, next_state, reward, not_done = replay_buffer.sample(batch_size)
with tf.GradientTape(persistent=True) as tape:
# Compute the targets for the Q and V critics
target_v_values = self.target_v_critic(next_state)
target_q1_values = self.target_q1_critic(next_state, self.actor(next_state)[0])
target_q2_values = self.target_q2_critic(next_state, self.actor(next_state)[0])
min_target_q_values = tf.minimum(target_q1_values, target_q2_values)
target_v_values -= alpha * self.actor.log_prob(self.actor(next_state)[0])
target_q_values = reward + not_done * gamma * (min_target_q_values - target_v_values)
# Compute the Q and V critic losses
v_values = self.v_critic(state)
q1_values = self.q1_critic(state, action)
q2_values = self.q2_critic(state, action)
v_loss = tf.reduce_mean((v_values - target_v_values)**2)
q1_loss = tf.reduce_mean((q1_values - target_q_values)**2)
q2_loss = tf.reduce_mean((q2_values - target_q_values)**2)
q_loss = q1_loss + q2_loss
# Compute the actor loss and update the actor
new_action, log_prob = self.actor(state)
actor_loss = tf.reduce_mean(alpha * log_prob - self.q1_critic(state, new_action))
actor_grads = tape.gradient(actor_loss, self.actor.trainable_variables)
self.actor_optim.apply_gradients(zip(actor_grads, self.actor.trainable_variables))
# Update the V and Q critics
v_grads = tape.gradient(v_loss, self.v_critic.trainable_variables)
self.v_critic_optim.apply_gradients(zip(v_grads, self.v_critic.trainable_variables))
q_grads = tape.gradient(q_loss, self.q1_critic.trainable_variables + self.q2_critic.trainable_variables)
self.q1_critic_optim.apply_gradients(zip(q_grads[:len(self.q1_critic.trainable_variables)], self.q1_critic.trainable_variables))
self.q2_critic_optim.apply_gradients(zip(q_grads[len(self.q1_critic.trainable_variables):], self.q2_critic.trainable_variables))
# Update the target V and Q critics
for target_v_param, v_param in zip(self.target_v_critic.trainable_variables, self.v_critic.trainable_variables):
target_v_param.assign(tau * v_param + (1 - tau) * target_v_param)
for target_q1_param, q1_param in zip(self.target_q1_critic.trainable_variables, self.q1_critic.trainable_variables):
target_q1_param.assign(tau * q1_param + (1 - tau) * target_q1_param)
for target_q2_param, q2_param in zip(self.target_q2_critic.trainable_variables, self.q2_critic.trainable_variables):
target_q2_param.assign(tau * q2_param + (1 - tau) * target_q2_param)
```
这里我们定义了`Actor`、`V_Critic`和`Q_Critic`三个类来实现Actor网络、V Critic网络和Q Critic网络,并在`SAC`类中定义了训练过程。在训练过程中,我们首先使用回放缓冲区采样一批数据,并使用`GradientTape`记录前向传递过程中的计算图,并计算出各个网络的损失。然后,我们使用优化器来更新Actor网络、V Critic网络和Q Critic网络的参数,并使用软更新的方式更新目标V Critic网络和目标Q Critic网络的参数。最后,我们返回损失值以便于监控训练过程。
请采用python基于Tensorflow架构提供基于最大熵的SAC强化学习算法代码
以下是基于TensorFlow构建的最大熵SAC强化学习算法的代码示例:
```
import tensorflow as tf
import numpy as np
import gym
# 定义常量
LR_A = 0.001 # actor的learning rate
LR_C = 0.002 # critic的learning rate
GAMMA = 0.9 # 折扣因子
TAU = 0.01 # 软更新的参数
MEMORY_CAPACITY = 10000 # 记忆库的大小
BATCH_SIZE = 32 # 批处理的大小
MAX_EPISODES = 200 # 最大的训练次数
MAX_EP_STEPS = 200 # 每次训练步数的最大值
# 定义Actor网络
class Actor(object):
def __init__(self, sess, n_features, n_actions, lr=0.001):
self.sess = sess
self.s = tf.placeholder(tf.float32, [None, n_features], 'state')
self.a = tf.placeholder(tf.float32, [None, n_actions], 'action')
self.td_error = tf.placeholder(tf.float32, None, 'td_error')
l1 = tf.layers.dense(self.s, 30, activation=tf.nn.relu)
mu = tf.layers.dense(l1, n_actions, activation=tf.nn.tanh)
sigma = tf.layers.dense(l1, n_actions, activation=tf.nn.softplus)
global_step = tf.Variable(0, trainable=False)
self.mu, self.sigma = tf.squeeze(mu*2), tf.squeeze(sigma+0.1)
self.normal_dist = tf.distributions.Normal(self.mu, self.sigma)
# 最大熵
self.log_prob = self.normal_dist.log_prob(self.a)
self.exp_v = self.log_prob * self.td_error
self.entropy = self.normal_dist.entropy()
self.loss = -(self.exp_v + 0.01*self.entropy)
self.train_op = tf.train.AdamOptimizer(lr).minimize(self.loss, global_step=global_step)
def learn(self, s, a, td):
self.sess.run(self.train_op, {self.s: s, self.a: a, self.td_error: td})
def choose_action(self, s):
s = s[np.newaxis, :]
return self.sess.run(self.normal_dist.sample(), {self.s: s})[0]
# 定义Critic网络
class Critic(object):
def __init__(self, sess, n_features, lr=0.002):
self.sess = sess
self.s = tf.placeholder(tf.float32, [None, n_features], 'state')
self.v_ = tf.placeholder(tf.float32, [None, 1], 'v_next')
self.r = tf.placeholder(tf.float32, None, 'r')
l1 = tf.layers.dense(self.s, 30, activation=tf.nn.relu)
self.v = tf.layers.dense(l1, 1, activation=None)
td = tf.reduce_mean(self.r + GAMMA * self.v_ - self.v)
self.td_error = tf.reduce_mean(tf.square(td))
self.train_op = tf.train.AdamOptimizer(lr).minimize(self.td_error)
def learn(self, s, r, s_):
v_ = self.sess.run(self.v, {self.s: s_})
td_error, _ = self.sess.run([self.td_error, self.train_op],
{self.s: s, self.v_: v_, self.r: r})
return td_error
# 经验回放
class Memory(object):
def __init__(self, capacity, dims):
self.capacity = capacity
self.data = np.zeros((capacity, dims))
self.pointer = 0
def store_transition(self, s, a, r, s_):
transition = np.hstack((s, a, r, s_))
index = self.pointer % self.capacity
self.data[index, :] = transition
self.pointer += 1
def sample(self, n):
assert self.pointer >= self.capacity, '记忆库数据不够多'
indices = np.random.choice(self.capacity, size=n)
return self.data[indices, :]
# 主函数
if __name__ == "__main__":
env = gym.make('Pendulum-v0')
env.seed(1)
np.random.seed(1)
tf.set_random_seed(1)
sess = tf.Session()
# 创建actor和critic网络
actor = Actor(sess, env.observation_space.shape[0], env.action_space.shape[0], LR_A)
critic = Critic(sess, env.observation_space.shape[0], LR_C)
sess.run(tf.global_variables_initializer())
# 创建记忆库
memory = Memory(MEMORY_CAPACITY, dims=env.observation_space.shape[0] * 2 + env.action_space.shape[0] + 1)
# 记录训练过程中的奖励和误差
reward_list = []
td_error_list = []
for i in range(MAX_EPISODES):
s = env.reset()
ep_reward = 0
ep_td_error = 0
for j in range(MAX_EP_STEPS):
env.render()
# actor选择动作
a = actor.choose_action(s)
# 执行动作,得到下一个状态和奖励
s_, r, done, info = env.step(a)
# 存储经验
memory.store_transition(s, a, r/10, s_)
# 如果记忆库数据够多,开始训练
if memory.pointer > MEMORY_CAPACITY:
# 抽取记忆库数据
batch_memory = memory.sample(BATCH_SIZE)
# 更新critic网络
td_error = critic.learn(batch_memory[:, :env.observation_space.shape[0]],
batch_memory[:, -env.observation_space.shape[0]:],
batch_memory[:, -env.observation_space.shape[0]-1:-1])
ep_td_error += td_error
# 更新actor网络
actor.learn(batch_memory[:, :env.observation_space.shape[0]],
batch_memory[:, env.observation_space.shape[0]:env.observation_space.shape[0] + env.action_space.shape[0]],
td_error)
# 状态更新
s = s_
ep_reward += r
# 如果一次训练结束,输出信息
if j == MAX_EP_STEPS-1:
print('Episode:', i, ' Reward: %i' % int(ep_reward), ' TD Error: %.4f' % float(ep_td_error/BATCH_SIZE))
reward_list.append(ep_reward)
td_error_list.append(ep_td_error/BATCH_SIZE)
break
# 关闭环境
env.close()
sess.close()
```
这段代码实现了一个简单的Pendulum-v0任务的最大熵SAC强化学习算法,其中Actor和Critic网络均采用了单层神经网络,可以根据需要进行修改。在训练过程中,会输出每一次训练的奖励和TD误差。