基于ddpg算法的机械臂接触力阻抗控制代码

这里提供一个基于DDPG算法的机械臂接触力阻抗控制的代码示例，需要使用Python和Tensorflow进行实现。

import numpy as np
import tensorflow as tf
import gym

# 定义DDPG算法的神经网络结构
class Critic:
    def __init__(self, sess, state_dim, action_dim):
        self.sess = sess
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.learning_rate = 0.001

        self.inputs, self.action, self.out = self.create_network()
        self.target_inputs, self.target_action, self.target_out = self.create_network()

        self.action_grads = tf.gradients(self.out, self.action)

        self.sess.run(tf.global_variables_initializer())

    def create_network(self):
        inputs = tf.placeholder(tf.float32, [None, self.state_dim])
        action = tf.placeholder(tf.float32, [None, self.action_dim])

        w1 = tf.Variable(tf.random_normal([self.state_dim, 64], stddev=0.01))
        b1 = tf.Variable(tf.zeros([64]))
        w2 = tf.Variable(tf.random_normal([64, 32], stddev=0.01))
        b2 = tf.Variable(tf.zeros([32]))
        w3 = tf.Variable(tf.random_normal([32, self.action_dim], stddev=0.01))
        b3 = tf.Variable(tf.zeros([self.action_dim]))

        h1 = tf.nn.relu(tf.matmul(inputs, w1) + b1)
        h2 = tf.nn.relu(tf.matmul(h1, w2) + b2)
        out = tf.tanh(tf.matmul(h2, w3) + b3)

        return inputs, action, out

    def train(self, inputs, action, target_q):
        return self.sess.run([self.out, self.action_grads], feed_dict={self.inputs: inputs, self.action: action, self.target_q: target_q})

    def predict(self, inputs, action):
        return self.sess.run(self.out, feed_dict={self.inputs: inputs, self.action: action})

class Actor:
    def __init__(self, sess, state_dim, action_dim, action_bound):
        self.sess = sess
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.action_bound = action_bound
        self.learning_rate = 0.0001

        self.inputs, self.out = self.create_network()

        self.sess.run(tf.global_variables_initializer())

    def create_network(self):
        inputs = tf.placeholder(tf.float32, [None, self.state_dim])

        w1 = tf.Variable(tf.random_normal([self.state_dim, 64], stddev=0.01))
        b1 = tf.Variable(tf.zeros([64]))
        w2 = tf.Variable(tf.random_normal([64, 32], stddev=0.01))
        b2 = tf.Variable(tf.zeros([32]))
        w3 = tf.Variable(tf.random_normal([32, self.action_dim], stddev=0.01))
        b3 = tf.Variable(tf.zeros([self.action_dim]))

        h1 = tf.nn.relu(tf.matmul(inputs, w1) + b1)
        h2 = tf.nn.relu(tf.matmul(h1, w2) + b2)
        out = tf.tanh(tf.matmul(h2, w3) + b3)

        scaled_out = tf.multiply(out, self.action_bound)

        return inputs, scaled_out

    def predict(self, inputs):
        return self.sess.run(self.out, feed_dict={self.inputs: inputs})

# 定义机械臂接触力阻抗控制环境
class ArmEnv:
    def __init__(self):
        self.action_bound = 100
        self.state_dim = 4
        self.action_dim = 1
        self.dt = 0.01
        self.arm_length = 1.0
        self.k = 10.0
        self.b = 1.0

    def reset(self):
        self.arm_pos = 0.0
        self.arm_vel = 0.0
        self.goal_pos = 1.0
        self.goal_vel = 0.0

        state = [self.arm_pos, self.arm_vel, self.goal_pos, self.goal_vel]

        return state

    def step(self, action):
        force = np.clip(action, -self.action_bound, self.action_bound)[0]

        arm_acc = (force - self.b*self.arm_vel - self.k*(self.arm_pos - self.goal_pos))/self.arm_length
        self.arm_vel += arm_acc*self.dt
        self.arm_pos += self.arm_vel*self.dt

        goal_acc = -self.k*(self.goal_pos - self.arm_pos)/self.arm_length
        self.goal_vel += goal_acc*self.dt
        self.goal_pos += self.goal_vel*self.dt

        reward = -1.0*np.abs(self.arm_pos - self.goal_pos)

        done = False

        if np.abs(self.arm_pos - self.goal_pos) < 0.05:
            done = True

        state = [self.arm_pos, self.arm_vel, self.goal_pos, self.goal_vel]

        return state, reward, done, None

    def render(self):
        pass

# 定义DDPG算法的训练过程
def training(sess, env, actor, critic):
    batch_size = 64
    max_episodes = 5000
    max_steps = 1000
    memory_size = 10000
    tau = 0.01
    gamma = 0.99

    memory = np.zeros((memory_size, env.state_dim*2 + env.action_dim + 1), dtype=np.float32)
    memory_counter = 0

    for i in range(max_episodes):
        state = env.reset()
        ep_reward = 0

        for j in range(max_steps):
            action = actor.predict(np.reshape(state, (1, env.state_dim))) + np.random.normal(0, 1, size=(1, env.action_dim))
            next_state, reward, done, _ = env.step(action[0])

            memory[memory_counter % memory_size, :] = np.hstack((state, action, [reward], next_state))
            memory_counter += 1

            if memory_counter > memory_size:
                minibatch = np.random.choice(memory_size, batch_size)
            else:
                minibatch = np.random.choice(memory_counter, batch_size)

            state_batch = memory[minibatch, :env.state_dim]
            action_batch = memory[minibatch, env.state_dim:env.state_dim+env.action_dim]
            reward_batch = memory[minibatch, env.state_dim+env.action_dim]
            next_state_batch = memory[minibatch, env.state_dim+env.action_dim+1:]

            target_q = critic.predict(next_state_batch, actor.predict(next_state_batch))
            y_i = []
            for k in range(batch_size):
                y_i.append(reward_batch[k] + gamma*target_q[k])
            y_i = np.reshape(y_i, (batch_size, 1))

            critic.train(state_batch, action_batch, y_i)

            action_grads = critic.sess.run(critic.action_grads, feed_dict={critic.inputs: state_batch, critic.action: actor.predict(state_batch)})
            actor.sess.run(actor.train_op, feed_dict={actor.inputs: state_batch, actor.action_grads: action_grads[0]})

            for k in range(len(critic.target_weights)):
                critic.sess.run(critic.target_weights[k].assign(tau*critic.weights[k]+(1-tau)*critic.target_weights[k]))
            for k in range(len(actor.target_weights)):
                actor.sess.run(actor.target_weights[k].assign(tau*actor.weights[k]+(1-tau)*actor.target_weights[k]))

            state = next_state
            ep_reward += reward

            if done:
                break

        print("Episode", i, "finished with reward:", ep_reward)

# 主函数
if __name__ == "__main__":
    env = ArmEnv()

    with tf.Session() as sess:
        actor = Actor(sess, env.state_dim, env.action_dim, env.action_bound)
        critic = Critic(sess, env.state_dim, env.action_dim)

        training(sess, env, actor, critic)

以上是一个简单的基于DDPG算法的机械臂接触力阻抗控制代码示例，供参考。