DQN加上人工势场法代码

DQN加上人工势场法是一种基于强化学习和人工势场法的路径规划算法。其中DQN是Deep Q-Network的缩写，是一种深度强化学习算法，可以通过学习最优的策略来解决在环境中的决策问题。而人工势场法是一种基于物理学中力场的概念，将物体在力场中的运动方式应用于路径规划中，以实现避障和寻路等目标。

下面是一个简单的DQN加上人工势场法的Python代码示例：

import gym
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.optimizers import Adam
from rl.agents.dqn import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory
from robot_env import RobotEnv

env = RobotEnv()
states = env.observation_space.shape
actions = env.action_space.n

model = Sequential()
model.add(Flatten(input_shape=(1, states)))
model.add(Dense(24, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(actions, activation='linear'))

memory = SequentialMemory(limit=50000, window_length=1)
policy = BoltzmannQPolicy()
dqn = DQNAgent(model=model, nb_actions=actions, memory=memory, nb_steps_warmup=10,
               target_model_update=1e-2, policy=policy)
dqn.compile(Adam(lr=1e-3), metrics=['mae'])
dqn.fit(env, nb_steps=5000, visualize=False, verbose=2)

weights_file = 'dqn_weights.h5f'
dqn.save_weights(weights_file, overwrite=True)

# 人工势场法
def potential_field(observation):
    goal_position = np.array([0.5, 0.5])
    obstacle_position = np.array([observation, observation])
    distance_vector = goal_position - obstacle_position
    distance = np.sqrt(np.sum(distance_vector ** 2))
    angle = np.arctan2(distance_vector, distance_vector)
    x_force = -np.cos(angle) / (distance ** 2)
    y_force = -np.sin(angle) / (distance ** 2)
    return np.array([x_force, y_force])

def get_action(observation):
    action_values = dqn.model.predict(np.array([observation]))
    action = np.argmax(action_values)
    force = potential_field(observation)
    force_magnitude = np.sqrt(np.sum(force ** 2))
    if force_magnitude > 0.01:
        force /= force_magnitude
        action += 4
    return action

obs = env.reset()
for i in range(200):
    action = get_action(obs)
    obs, reward, done, info = env.step(action)
    if done:
        break
env.close()