DQN的算法流程及代码
时间: 2023-03-11 11:18:50 浏览: 147
DQN(深度强化学习)的算法流程首先是采取一个行动,然后从环境中获取反馈,根据反馈来更新网络参数,以期望获得最大化的奖励。代码方面,可以使用TensorFlow或PyTorch等深度学习框架来实现DQN算法。
相关问题
dqn算法 的python代码
下面是一个简单的DQN算法的Python代码示例:
```python
import random
import numpy as np
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
class DQNAgent:
def __init__(self, state_size, action_size):
self.state_size = state_size
self.action_size = action_size
self.memory = deque(maxlen=2000)
self.gamma = 0.95 # 衰减率
self.epsilon = 1.0 # 探索率
self.epsilon_min = 0.01
self.epsilon_decay = 0.995
self.learning_rate = 0.001
self.model = self._build_model()
def _build_model(self):
model = Sequential()
model.add(Dense(24, input_dim=self.state_size, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(self.action_size, activation='linear'))
model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
return model
def remember(self, state, action, reward, next_state, done):
self.memory.append((state, action, reward, next_state, done))
def act(self, state):
if np.random.rand() <= self.epsilon:
return random.randrange(self.action_size)
act_values = self.model.predict(state)
return np.argmax(act_values[0])
def replay(self, batch_size):
minibatch = random.sample(self.memory, batch_size)
for state, action, reward, next_state, done in minibatch:
target = reward
if not done:
target = (reward + self.gamma * np.amax(self.model.predict(next_state)[0]))
target_f = self.model.predict(state)
target_f[0][action] = target
self.model.fit(state, target_f, epochs=1, verbose=0)
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
def load(self, name):
self.model.load_weights(name)
def save(self, name):
self.model.save_weights(name)
# 使用示例
env = gym.make('CartPole-v1')
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
agent = DQNAgent(state_size, action_size)
done = False
batch_size = 32
for e in range(EPISODES):
state = env.reset()
state = np.reshape(state, [1, state_size])
for time in range(500):
env.render()
action = agent.act(state)
next_state, reward, done, _ = env.step(action)
reward = reward if not done else -10
next_state = np.reshape(next_state, [1, state_size])
agent.remember(state, action, reward, next_state, done)
state = next_state
if done:
print("episode: {}/{}, score: {}, e: {:.2}"
.format(e, EPISODES, time, agent.epsilon))
break
if len(agent.memory) > batch_size:
agent.replay(batch_size)
if e % 10 == 0:
agent.save("./save/cartpole-dqn.h5")
```
注意:这只是一个简单的DQN算法示例,实际应用中可能需要根据具体问题进行调整和改进。
dqn算法matlab代码
下面是使用DQN算法解决CartPole问题的MATLAB代码:
```matlab
% 首先定义神经网络模型
numStates = 4;
numActions = 2;
layers = [ ...
imageInputLayer([numStates 1 1])
fullyConnectedLayer(10)
reluLayer
fullyConnectedLayer(numActions)
regressionLayer];
% 定义DQN算法的超参数
maxEpisodes = 500;
maxSteps = 500;
epsilon = 0.1;
gamma = 0.95;
memorySize = 10000;
batchSize = 64;
learnRate = 0.001;
targetUpdateFrequency = 10;
% 创建DQN对象
dqn = rlDQNAgent(layers, ...
"Epsilon", epsilon, ...
"Gamma", gamma, ...
"MemorySize", memorySize, ...
"MiniBatchSize", batchSize, ...
"LearnRate", learnRate);
% 定义CartPole环境
env = rlPredefinedEnv("CartPole-Discrete");
% 训练DQN
trainingStats = train(dqn, env, ...
"MaxEpisodes", maxEpisodes, ...
"MaxStepsPerEpisode", maxSteps, ...
"Plots","training-progress", ...
"TargetUpdateFrequency", targetUpdateFrequency);
% 测试DQN
testStats = sim(env, dqn, "MaxSteps", maxSteps);
```
这段代码定义了一个包含一个输入层、一个全连接层、一个ReLU激活函数、一个输出层和一个回归损失函数的神经网络模型。然后使用这个模型创建了一个DQN对象,并定义了一些超参数,包括最大训练次数、最大步数、贪心策略的epsilon值、折扣因子gamma、经验回放池大小、批量大小、学习率和目标网络更新频率。接下来,使用rlPredefinedEnv函数创建了CartPole环境,并使用train函数训练了DQN。最后,使用sim函数测试了训练好的DQN在CartPole环境上的表现。
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